System and method for enhancing speech of target speaker from audio signal in an ear-worn device using voice signatures

ABSTRACT

An ear-worn device is provided that operates to isolate and individually treat the received speech of a target speaker or multiple target speakers from an audio input signal detected in a multi-speaker environment. The ear-worn device uses a machine learning model that receives a voice signature of each of one or more target speakers as input signals, to identify and isolate the component of the audio input signal attributable to the target speaker(s). Once isolated, the target speaker&#39;s speech may be enhanced, de-emphasized, or otherwise processed in a manner desired by the wearer of the ear-worn device. The wearer may use an external electronic device, e.g., a phone, to select one or more target speakers in a conversation and/or configure various settings associated with processing the speech on the ear-worn device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation claiming the benefit under 35 U.S.C.§ 120 of U.S. patent application Ser. No. 18/097,154, entitled “Systemand Method for Enhancing Speech of Target Speaker from Audio Signal inan Ear-Worn Device Using Voice Signatures,” filed Jan. 13, 2023, whichis herein incorporated by reference in its entirety.

U.S. patent application Ser. No. 18/097,154 is a continuation-in-part,claiming the benefit under 35 U.S.C. § 120, of U.S. application Ser. No.17/576,718, entitled “Method, Apparatus and System for Neural NetworkHearing Aid,” filed Jan. 14, 2022, which is herein incorporated byreference in its entirety.

U.S. patent application Ser. No. 18/097,154 is a continuation-in-part,claiming the benefit under 35 U.S.C. § 120, of U.S. application Ser. No.17/576,746, entitled “Method, Apparatus and System for Neural NetworkHearing Aid,” filed Jan. 14, 2022, which is herein incorporated byreference in its entirety.

U.S. patent application Ser. No. 18/097,154 is a continuation-in-part,claiming the benefit under 35 U.S.C. § 120, of U.S. application Ser. No.17/576,893, entitled “Method, Apparatus and System for Neural NetworkHearing Aid,” filed Jan. 14, 2022, which is herein incorporated byreference in its entirety.

U.S. patent application Ser. No. 18/097,154 is a continuation-in-part,claiming the benefit under 35 U.S.C. § 120, of U.S. application Ser. No.17/576,899, entitled “Method, Apparatus and System for Neural NetworkHearing Aid,” filed Jan. 14, 2022, which is herein incorporated byreference in its entirety.

U.S. patent application Ser. No. 18/097,154 is a continuation-in-part ofInternational Patent Application Serial No. PCT/US2022/012567, entitled“Method, Apparatus and System for Neural Network Hearing Aid,” filedJan. 14, 2022, which is herein incorporated by reference in itsentirety.

U.S. patent application Ser. No. 18/097,154 claims the benefit under 35U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No.63/305,676 filed Feb. 1, 2022, and entitled “SYSTEM AND METHOD FORENHANCING SPEECH OF TARGET SPEAKER FROM AUDIO SIGNAL IN AN EAR-WORNDEVICE USING VOICE SIGNATURES,” which is herein incorporated byreference in its entirety.

BACKGROUND Field

The present application relates to ear-worn speech enhancement devices.

Related Art

Hearing aids are used to help those who have trouble hearing to hearbetter. Typically, hearing aids amplify received sound. Some hearingaids attempt to remove environmental noise from the incoming sound.

BRIEF SUMMARY

Some embodiments provide for a method for selectively processing with anear-worn device a target speaker's speech from an audio signalcomprising the target speaker's speech and speech from additionalspeakers. The ear-worn device includes a processor and a microphonecoupled to the processor. The method comprises: detecting the audiosignal with the microphone of the ear-worn device; providing the audiosignal detected by the microphone of the ear-worn device to theprocessor of the ear-worn device; and increasing, with the processor ofthe ear-worn device, a signal-to-noise ratio (SNR) of the targetspeaker's speech by processing the audio signal with a machine learningmodel using a voice signature of the target speaker.

Some embodiments provide for an apparatus comprising a processor and amicrophone coupled to the processor. The apparatus is an ear-worndevice. The processor is configured to selectively process a targetspeaker's speech from an audio signal comprising the target speaker'sspeech and speech from additional speakers. The processing includes:detecting the audio signal with the microphone of the ear-worn device;providing the audio signal detected by the microphone of the ear-worndevice to the processor of the ear-worn device; and increasing, with theprocessor of the ear-worn device, a signal-to-noise ratio (SNR) of thetarget speaker's speech by processing the audio signal with a machinelearning model using a voice signature of the target speaker.

Some embodiments provide for a method for selectively processing with anear-worn device a target speaker's speech from an audio signalcomprising the target speaker's speech and speech from additionalspeakers. The ear-worn device includes a processor and a microphonecoupled to the processor. The method comprises: detecting the audiosignal with the microphone of the ear-worn device; providing the audiosignal detected by the microphone of the ear-worn device to theprocessor of the ear-worn device; and increasing, with the processor ofthe ear-worn device, a signal-to-noise ratio (SNR) of the targetspeaker's speech by processing the audio signal with a machine learningmodel using a voice signature of the target speaker.

Some embodiments provide for an apparatus comprising a processor and amicrophone coupled to the processor. The apparatus is an ear-worndevice. The processor is configured to selectively process a targetspeaker's speech from an audio signal comprising the target speaker'sspeech and speech from additional speakers. The processing includes:detecting the audio signal with the microphone of the ear-worn device;providing the audio signal detected by the microphone of the ear-worndevice to the processor of the ear-worn device; and increasing, with theprocessor of the ear-worn device, a signal-to-noise ratio (SNR) of thetarget speaker's speech by processing the audio signal with a machinelearning model using a voice signature of the target speaker.

Some embodiments provide for a method for operating a mobile processingdevice operatively couplable to an ear-worn device. The methodcomprises: wirelessly transmitting, from the mobile processing device tothe ear-worn device, a voice signature of at least one target speaker.

Some embodiments provide for an apparatus comprising a processor, wherethe apparatus is operatively couplable to an ear-worn device. Theapparatus is a mobile processing device. The processor is configured to:wirelessly transmit, from the mobile processing device to the ear-worndevice, a voice signature of at least one target speaker.

Some embodiments provide for a system comprising: a hearable devicecomprising a microphone configured to receive an audio signal comprisingtemporally overlapping speech components from multiple speakers; and atleast one processor configured to process the audio signal received bythe microphone to identify a target speaker among the multiple speakers.

Some embodiments provide a method of selectively processing, with anear-worn device including a processor and a microphone coupled to theprocessor, a target speaker's speech from an audio signal. The methodcomprises detecting the audio signal with the microphone of the ear-worndevice; providing the audio signal detected by the microphone of theear-worn device to the processor of the ear-worn device; and isolating,with the processor of the ear-worn device, a component of the audiosignal representing speech; determining, with the processor of theear-worn device, that the component of the audio signal representingspeech represents speech of a target or non-target speaker; and applyinga relative gain to the audio signal in dependence on determining thatthe component of the audio signal represents speech of the target ornon-target speaker.

Some embodiments provide for non-transitory computer-readable mediumincluding instructions that when executed by a processor, perform one ormore of the methods listed above.

BRIEF DESCRIPTION OF DRAWINGS

Various aspects and embodiments of the application will be describedwith reference to the following figures. It should be appreciated thatthe figures are not necessarily drawn to scale. Items appearing inmultiple figures are indicated by the same reference number in all thefigures in which they appear.

FIG. 1 illustrates an example multi-speaker environment and an audiosystem including an ear-worn device and a separate electronic device,according to a non-limiting embodiment of the present application.

FIG. 2 illustrates communication between an ear-worn device and aseparate electronic device, according to a non-limiting embodiment ofthe present application.

FIG. 3 illustrates a system with an ear-worn device and a portableelectronic device for selectively enhancing speech from a targetspeaker, according to a non-limiting embodiment of the presentapplication.

FIG. 4A illustrates example components of an ear-worn device that may beconfigured to enhance speech of a target speaker in a multi-speakerenvironment, according to a non-limiting embodiment of the presentapplication.

FIG. 4B illustrates example components of a variation of the ear-worndevice in FIG. 4A that may be configured to enhance speech of a targetspeaker in a multi-speaker environment, according to a non-limitingembodiment of the present application.

FIGS. 5A-5B illustrate example components of an ear-worn device havingtwo microphones, according to a non-limiting embodiment of the presentapplication.

FIGS. 6A and 6B illustrate an example configuration of a voice isolationnetwork, according to a non-limiting embodiment of the presentapplication.

FIG. 6C illustrate an example signal stream that may be concatenatedfrom an audio signal and voice signature(s) of target speaker(s),according to a non-limiting embodiment of the present application.

FIG. 7A is a flowchart of an example method of operation of an ear-worndevice configured to selectively isolate speech from a target speakerwithin a multi-speaker environment, according to a non-limitingembodiment of the present application.

FIG. 7B is a variation of the example method in FIG. 7A of operation ofan ear-worn device configured to selectively isolate speech from atarget speaker within a multi-speaker environment, according to anon-limiting embodiment of the present application.

FIG. 8 is a block diagram illustrating training and deployment of avoice isolation machine learning model for isolating speech from atarget speaker, according to a non-limiting embodiment of the presentapplication.

FIG. 9 illustrates a block diagram of a system-on-chip (SOC) packagethat may be implemented in an ear-worn device, according to anon-limiting embodiment of the present application.

FIG. 10 is a block diagram illustrating a portion of a circuitryconfiguration of an electronic device operable to extract voicesignature(s), according to a non-limiting embodiment of the presentapplication.

FIG. 11A illustrates an example graphical user interface that may beimplemented in an electronic device to select one or more targetspeakers, according to a non-limiting embodiment of the presentapplication.

FIG. 11B illustrates a block diagram of an example process forimplementing the example graphical user interface of FIG. 11A, accordingto a non-limiting embodiment of the present application.

FIG. 11C illustrates an example graphical user interface that may beimplemented in an electronic device to select one or more targetspeakers, according to a non-limiting embodiment of the presentapplication.

FIG. 11D illustrates a block diagram of an example process forimplementing the example graphical user interface of FIG. 11C, accordingto a non-limiting embodiment of the present application.

FIG. 11E illustrates an example graphical user interface that may beimplemented in an electronic device to add a new speaker to a registryof known speakers, according to a non-limiting embodiment of the presentapplication.

FIG. 11F illustrates a block diagram of an example process forimplementing the example graphical user interface of FIG. 11E, accordingto a non-limiting embodiment of the present application.

FIG. 12 illustrates a block diagram of an example process for collectinga voice signature of a speaker, according to a non-limiting embodimentof the present application.

FIG. 13 is a block diagram illustrating training and deployment of avoice signature machine learning model for extracting voice signature(s)from speech data, according to a non-limiting embodiment of the presentapplication.

FIG. 14 illustrates an example of a computing system that may beimplemented in an electronic device to implement various embodimentsdescribed in the present application.

FIG. 15 illustrates an example circuit including a voice isolationnetwork and a voice signature network, according to a non-limitingembodiment of the present application.

FIG. 16 illustrates an example of a voice isolation network having avoice isolation model for de-noising an input audio signal anddetermining an embedding of the input audio signal.

FIG. 17 illustrates a voice isolation and classification networkaccording to some embodiments of the present technology.

DETAILED DESCRIPTION

Aspects of the present technology provide hearing systems and methodsfor de-noising a received audio signal, identifying the presence of atarget or non-target speaker's speech in the received audio signal, andprocessing the received audio signal for output to a listener based ondetection of the target or non-target speaker's speech. The hearingsystem may include an ear-worn device, such as a hearing aid, and aseparate electronic device, such as a mobile phone or tablet, incommunication with the ear-worn device. The target speaker may be one ormore conversation partners of the wearer of the ear-worn device, while anon-target speaker may be another conversation partner or may be thewearer of the ear-worn device herself. The ear-worn device may processthe received audio signal using a machine learning model. In someembodiments, the machine learning model also receives a voice signatureof the target and/or non-target speaker(s). The voice signature may beused by the machine learning model to identify the speech of the targetand/or non-target speaker(s), and the hearing system may thenpreferentially process the speech of the target and/or non-targetspeaker(s). In some embodiments, the machine learning model may processthe received audio signal by de-noising the audio signal and determiningan embedding of the audio signal, which may be compared to a referenceembedding representing a voice signature. The audio signal may then beprocessed differently depending on whether the embedding determined fromthe received audio signal matches the reference embedding. For example,the wearer's own voice may be attenuated, or the speech of aconversation partner may be enhanced.

According to an aspect of the technology described herein, an ear-worndevice is provided that operates to isolate and individually treat thereceived speech of a target speaker or multiple target speakers from anaudio input signal detected in a multi-speaker environment. The ear-worndevice, which is a hearing aid in some embodiments, uses a machinelearning model that receives a voice signature of each of one or moretarget speakers as input signals, to identify and isolate the componentof the audio input signal attributable to the target speaker(s). Onceisolated, the target speaker's speech may be enhanced, de-emphasized, orotherwise processed in a manner desired by the wearer of the ear-worndevice. As a result, the wearer of the ear-worn device can have apositive experience in multi-speaker environments.

The inventors have recognized that conventional hearing aids do notperform well in multi-speaker environments. Some hearing aids amplifyall received sounds. Some hearing aids attempt to filter out ambientnoise and amplify all speech received. The inventors have appreciatedthat such approaches perform inadequately in some hearing aid usescenarios including multi-speaker environments, which is exacerbated bypoor performance at filtering out ambient noise. Examples ofmulti-speaker environments include family gatherings, meals withmultiple people, conference meetings, networking events, playgroundsettings, and school classrooms. In these and other multi-speakerenvironments, an individual often wishes to listen to a subset of thespeakers present. For instance, multiple conversations may occursimultaneously between different people seated at a table, and a hearingaid wearer may wish to pay attention to one of the conversations and notthe others. Hearing aids that simply amplify all sound or that amplifyall speech are inadequate in such a setting, as they are in othermulti-speaker environments, because they fail to provide the user withthe desired level of hearing focus.

The inventors have also recognized that conventional hearing aids do notperform well at reducing or excluding the wearer's own voice. Asdescribed above, some hearing aids amplify all received sounds,including the wearer's own speech. Such behavior can worsen the wearer'sexperience. People are used to hearing their own voice both travelingthrough the air into their ears and traveling through the bones of theirhead to their ears, with both signals arriving at very low latency. Thebone conduction path typically has a different frequency characteristicthan the air conduction path. When also played back through a hearingaid, an amplified version of the person's voice is typically at a delayof a few milliseconds and has the frequency characteristics of the airconduction path. Hearing aid wearers typically find the experience ofhearing themselves more loudly, with an uncharacteristic sound, to beinitially very unnatural. As a result, people can be dissuaded fromwearing a hearing aid even when they have poor hearing.

The consequences of poor hearing aid performance are significant.Hearing is a fundamental sense which impacts how people experience theirsettings and interactions with others. Poor hearing aid experience leadsto the hearing aid user withdrawing from those activities in which thehearing aid does not work well or avoiding such activities altogether.Withdrawing from these common and innately human multi-speakerenvironments can be detrimental to family connections and friendships,among other things, and can contribute to the further health andemotional decline of the individual hard of hearing.

Aspects of the present application provide hearing aids or otherear-worn devices that provide a wearer with a positive experience inmulti-speaker environments by aiding the wearer in focusing on thespeech of a desired subset of the speakers in the multi-speakerenvironment. For example, the speech of one or more target speakers maybe selectively amplified. The speech of one or more non-target speakersmay be reduced or eliminated. For instance, the ear-worn device wearermay not want to hear his or her own voice, and therefore may select forthe ear-worn device to deemphasize his or her own speech. In at leastsome embodiments, the wearer can select the target speaker(s) ornon-target speaker(s).

Aspects of the present application provide an intelligent ear-worndevice that provides a true audio experience by selectively isolatingspeech from one or more target speakers in a multi-speaker environmentwithout noticeable delay of the audio signal containing the speech. Theear-worn device may feature a temporal lobe on a chip executing amachine learning model that analyzes an incoming audio signal,identifies and isolates speech in the incoming audio signal attributableto the target speaker, and presents that speech to the wearer. Theisolated speech is presented to the wearer within a time amounting to nomore than an insignificant delay from the perspective of the wearer, aswould not negatively impact natural conversation. Thus, the machinelearning model operates to identify and isolate the speech within thattime.

According to an aspect of the technology described herein, the inputaudio signal may be segmented into small samples (segments), which areprocessed sequentially. An audio segment may be provided to the machinelearning model to generate isolated speech. While the isolated speech isfurther processed and played back to the wearer of the ear-worn device,the subsequent audio segment is being provided to and processed by themachine learning model. Thus, when the playback of the current audiosegment is completed, the subsequent audio segment outputted by themachine learning model will be ready for further processing andplayback. Such technology, combined with a choice of machine learningmodel, for example, a recurrent neural network, may facilitate theear-worn device processing the speech signal with a machine learningmodel without introducing noticeable delay to the wearer's ears.

The inventors have recognized that voice signatures may be used byear-worn devices to isolate the speech of one or more target speakers ornon-target speakers. Individual speakers typically exhibit unique speechcharacteristics. The unique speech characteristics can be used touniquely identify the respective speaker, and thus may serve as a voicesignature. The voice signature may take various forms usable by aprocessor of an ear-worn device. The ear-worn device may process thevoice signature in combination with an audio signal received by theear-worn device to identify and isolate the component of the audiosignal attributable to the speaker associated with the voice signature.The processing may be performed using a machine learning model executingon the ear-worn device.

According to an aspect of the technology described herein, an ear-worndevice is configured to use a machine learning model that operates on avoice signature and an input audio signal containing speech frommultiple speakers to isolate a component of the speech representingspeech of a target speaker associated with the voice signature. Themachine learning model receives the input audio signal as one inputsignal and a voice signature of the target speaker as a second inputsignal, and outputs the isolated speech component. The voice signatureis a feature vector including frequency domain components. The voicesignature may be used by the machine learning model to generate acomplex ideal ratio mask that may be applied to the input audio signalto isolate the target speaker's speech from the input audio signal.After isolating the target speaker's speech, that speech may bepreferentially treated (e.g., amplified) to produce an enhanced outputaudio signal for the ear-worn device.

According to an aspect of the technology described herein, the voicesignatures used by the machine learning model of an ear-worn device maybe provided by a separate electronic device that, together with theear-worn device, forms an audio system. The separate electronic devicemay be a smartphone, tablet computer, personal digital assistant (PDA),or other device in communication with the ear-worn device. The separateelectronic device may provide one or more voice signatures to theear-worn device, for example in response to user selection. For example,the separate electronic device may contain a registry of voicesignatures from which the user of the ear-worn device may select. Theprovided voice signature(s) may serve as an input to the machinelearning model executed by the ear-worn device to isolate speechattributable to the speaker associated with the voice signature(s).

Thus, according to an aspect of the technology described herein, aregistry of voice signatures is provided. The voice signature registryincludes one or more voice signatures associated with known speakers,who are potential target speakers and non-target speakers for anear-worn device wearer. One or more signatures may be selected from theregistry for use in a machine learning model of an ear-worn device, toallow the ear-worn device to identify and isolate speech attributable tospeakers associated with the selected voice signature(s). In someembodiments, the machine learning model may receive a subset of thevoice signatures associated with the registry representing selectedknown speakers that are present in a given conversation.

The voice signature registry may be stored in various locations. In oneembodiment, the voice signature(s) are stored on the external electronicdevice (e.g., the smartphone). In some embodiments, a user of theexternal electronic device may be the wearer of the ear-worn device. Ina given conversation (e.g., in a multi-speaker environment), a user ofthe external electronic device may select the target speaker(s) on theexternal electronic device from among known speakers in the registry,e.g., from an option list (menu). Alternatively, and/or additionally,the external electronic device may automatically identify targetspeakers by determining whether the input audio signal in a conversationincludes speech components of one or more known speakers in theregistry. The user may then be presented with a menu of the identifiedtarget speakers from which to select. Once the target speaker(s) areidentified and/or selected, the voice signature(s) associated with thetarget speaker(s) are sent to the ear-worn device. In one embodiment,the voice signature(s) are stored on the ear-worn device itself and canbe called from memory. In such case, instead of transmitting the voicesignature(s) of the target speaker(s) to the ear-worn device, theexternal electronic device may transmit identifier(s) of the targetspeaker(s) to the ear-worn device, which in turn can retrieve the storedvoice signature(s) of target speaker(s) based on the identifiers.

The voice signatures associated with the registry of known speakers maybe collected in various ways. In some embodiments, the voice signaturesmay be extracted from audio input using another machine learning model,e.g., a voice signature machine learning model implemented in a voicesignature network separate from the machine learning model used by theear-worn device to isolate speech from a received audio signal. A sampleof speech from the target speaker may be provided as input to the voicesignature network. The voice signature network may output amulti-dimensional feature vector representing the voice signature forthat target speaker. The machine learning model used to extract voicesignatures may operate on the separate electronic device (e.g., thesmartphone) in some embodiments. In some other embodiments, the machinelearning model used to extract voice signatures may operate on theear-worn device.

The sample speech of the target speaker used by the voice signaturenetwork to extract the voice signature may be obtained in variousmanners. In some examples, input audio signal including the samplespeech may be detected using the microphone of the external electronicdevice. In other examples, the input audio signal including the samplespeech may be detected using the microphone of the ear-worn device, andthe ear-worn device then transmits the input audio signal to theexternal electronic device. The input audio signal may be transmitted tothe external electronic device wirelessly. In some embodiments, thetarget speaker may be provided a microphone and may read a predeterminedscript. Alternatively, the target speaker may provide a speech sampleonline. In a further alternative, the speech may be dynamicallyextracted from audio input to the ear-worn device wearer. For instance,while having a conversation with a given speaker, the ear-worn devicemay collect an audio sample and provide that sample to the voicesignature network, which may extract the voice signature.

In some embodiments, multiple target speakers may be identified in amulti-speaker conversation. In the multi-speaker conversation, multipletarget speakers may speak at different or overlapping times. In someembodiments, the ear-worn device is configurable to isolate the speechof multiple target speakers. The neural network may receive multiplevoice signatures as input(s) and analyze the incoming audio signal usingthose voice signatures to isolate speech components from the input audiosignal attributable to the multiple target speakers.

As should be appreciated from the foregoing, according to someembodiments of the technology described herein, an ear-worn device isconfigured to use a voice isolation machine learning model that operateson a voice signature to isolate a component of the speech representingspeech of a target speaker associated with the voice signature. Thevoice signature may be obtained from a separate machine learning model,such as a voice signature machine learning model, configured to extractthe voice signature from one or more reference audio clips representingspeech of the target or non-target speaker. Such an embodiment may beadvantageous for multiple reasons. As a non-limiting example, the voicesignature machine learning model may be implemented on an electronicdevice different from the ear-worn device, thereby reducing thecomputational complexity of implementing multiple machine learningmodels on the ear-worn device, while retaining voice isolationcapabilities.

According to an aspect of the technology, an ear-worn device isconfigured to use a voice isolation and classification machine learningmodel that operates on an input audio signal to both (a) de-noise theinput audio signal and (b) determine an embedding of the input audiosignal. The voice isolation and classification machine learning modelmay be used to determine an embedding representative of a target ornon-target speaker, for example by supplying the voice isolation andclassification machine learning model with a sample audio clip from thetarget or non-target speaker. Subsequently, the voice isolation andclassification model may operate on input audio signals to de-noise theinput audio signal and determine an embedding of the input audio signal,and the determined embedding may be compared to the embedding determinedpreviously to correspond to the target or non-target speaker. The mannerof processing the de-noised audio signal may depend on whether the twoembeddings are found to match. For example, if the non-target speaker isthe hearing aid wearer, then the de-noised audio signal may beattenuated when the two embeddings are determined to match, whereas thede-noised audio signal may be amplified if the two embeddings do notindicate a match.

As described above, according to an aspect of the present application,the ear-worn device may be configured to suppress the wearer's ownvoice. Using a voice signature of the wearer, the machine learning modelmay suppress such audio. The wearer of the ear-worn device may selectsuch operation when he or she wishes to only hear the speech of his orher conversation partners.

The aspects and embodiments described above, as well as additionalaspects and embodiments, are described further below. These aspectsand/or embodiments may be used individually, all together, or in anycombination of two or more, as the disclosure is not limited in thisrespect.

As described above, aspects of the technology described herein providean ear-worn device that operates to isolate and individually treat thereceived speech of a target speaker or multiple target speakers from anaudio input signal detected in a multi-speaker environment. FIG. 1illustrates a multi-speaker environment and an audio system including anear-worn device of the type described and a separate electronic device.The multi-speaker environment 100 includes ear-worn device wearer 102and other individuals, including a target speaker 104 and two non-targetspeakers 106 a and 106 b. The ear-worn device wearer 102 has an ear-worndevice 108 and a separate electronic device 110. The ear-worn device 108and electronic device 110 in combination represent an audio system.

The illustrated setting of the multi-speaker environment 100 is one inwhich multiple individuals may speak simultaneously. In the illustratedexample, the individuals are seated around a table. For instance, theillustrated individuals may be having a meeting, playing a game, or maybe having a meal. Other scenarios are possible as well, as the specificsetting is not limiting of the various aspects of the technologydescribed herein. In this context, multiple of the illustratedindividuals may be speaking at the same time. For example, the ear-worndevice wearer 102 and the target speaker 104 may be having aconversation, and the non-target speakers 106 a and 106 b may be havinga conversation. There may be other reasons that multiple of theillustrated individuals may be speaking simultaneously.

The ear-worn device wearer 102 is wearing an ear-worn device 108 whichdetects sound and outputs an audio signal to the ear-worn device wearer102. For example, the ear-worn device wearer 102 may be hard of hearing,and the ear-worn device 108 may be a hearing aid. The ear-worn device108 may be capable of detecting environment sound as well as the speechof the target speaker 104 and non-target speakers 106 a and 106 b. Theear-worn device wearer, however, may wish to listen to the targetspeaker 104, and not the non-target speakers 106 a and 106 b. Theear-worn device 108 may detect the speech of all the illustratedspeakers, but output to the ear-worn device wearer 102 an audible outputsignal representing the speech of the target speaker 104, with thespeech of non-target speakers 106 a and 106 b reduced or eliminated.

The ear-worn device 108 and the electronic device 110 may work incombination to allow the ear-worn device wearer 102 to listen to onlythe speech of target speaker 104. The electronic device 110 may store aregistry of voice signatures. For example, the electronic device 110 maystore voice signatures for each of the target speaker 104, non-targetspeaker 106 a, and non-target speaker 106 b. When the ear-worn devicewearer 102 wants to listen to only target speaker 104, the ear-worndevice wearer 102 may select the voice signature for target speaker 104from the voice signature registry on electronic device 110. Theelectronic device 110 may provide the voice signature to the ear-worndevice 108, which may use the provided voice signature to identify andisolate speech attributable to target speaker 104 from a detected audiosignal containing speech components attributable to target speaker 104and at least one of non-target speaker 106 a or non-target speaker 106b.

FIG. 2 illustrates an example implementation of the audio systemillustrated in FIG. 1 . As shown, the audio system 200 includes theear-worn device 108 and the electronic device 110. In this example, theear-worn device is a hearing aid and the electronic device 110 is asmartphone. The electronic device 110 includes a display screen 202which can display entries 204 of a voice registry. The ear-worn devicewearer can select the entry of interest to be the target speaker or aspeaker the ear-worn device wearer would prefer to selectively not hear.The voice signature(s) corresponding to the selected speaker(s) can besent to the ear-worn device 102 via a wireless communication link 206.The ear-worn device 108 may use the received voice signature from theelectronic device to process audio signals detected from themicrophone(s) of the ear-worn device to isolate speech attributable tothe selected speaker(s). The isolated speech may be output to theear-worn device wearer 102 through the speaker device(s) (e.g.,receiver(s)) of the ear-worn device.

FIG. 3 illustrates a system with an ear-worn device and a portableelectronic device for selectively enhancing speech from a targetspeaker, according to a non-limiting embodiment of the presentapplication. Audio system 300 may be an example implementation of thesystem shown in FIGS. 1 and 2 . For example, audio system 300 mayinclude an ear-worn device 302 and electronic device 304. The ear-worndevice 302 may be an example implementation of the ear-worn device 108of FIGS. 1 and 2 . Ear-worn device 302 as described in FIG. 3 may havevarious forms. For example, the ear-worn device may be a hearing aid ora headphone, or any suitable wearable audio device. Additionally,ear-worn device 302 may include a communication port 314 configured tocommunicate (e.g., wired or wirelessly) with an external device andexchange data with the external device, such as electronic device 304.Electronic device 304 may be an example implementation of the electronicdevice 110 of FIGS. 1 and 2 . For example, electronic device 304 may bea smart phone, or any suitable portable electronic device associatedwith the wearer of the ear-worn device.

In some non-limiting examples, ear-worn device 302 may include amicrophone 308 and a speaker device (e.g., a receiver) 312. Microphone308 may be configured to detect audio signal 336 from sound (e.g.,speech). For example, the audio signal may include temporallyoverlapping speech components from multiple speakers. Ear-worn device302 may be capable of processing the audio signal 336 detected by themicrophone 308 to isolate a component of the audio signal representingthe target speaker's speech from among the temporally overlapping speechcomponents from multiple speakers. In some embodiments, ear-worn device302 may be configured to process the audio signal 336 with a voiceisolation network using a voice signature of the target speaker. In someexamples, the voice signature of a speaker may be a multi-dimensionalfeature vector. The voice signature may contain data infrequency-domain, time-domain, or any suitable data that may berepresentative of different speakers. Receiver 312 may include an audiooutput device configured to playback the output from the voice isolationnetwork to the ear-worn device wearer, such as a speaker device.

Voice signature of a speaker may be a unique digital representation of aspeaker, where voice signatures of different speakers are distinctive.In some embodiments, the system may use a trained voice signaturenetwork to extract the voice signature of a speaker from an audio signalcontaining speech of the speaker. When the voice signature network isproperly trained, the voice signatures for different speakers (i.e.,speaker embeddings) extracted using the voice signature network may bedistinctive and the voice signatures extracted from different audiosignal including speech of the same speaker may be the same. Using thevoice signature that is unique to a selected target speaker, the voiceisolation network may isolate the speech components from an audiosignal, where the isolated speech component is attributable to theselected target speaker. In some embodiments, each of the voicesignature network and the voice isolation network may include adifferent machine learning model. For example, the voice signaturenetwork may include a voice signature machine learning model configuredto receive an audio signal as input and output voice signature of thespeaker whose speech is included in the audio signal. The voiceisolation network may include a voice isolation machine learning modelconfigured to receive two inputs, an audio signal including a targetspeaker(s) and a voice signature(s) of the target speaker(s), and outputisolated speech of the target speaker(s). Details of the voice isolationnetwork will be further described in with reference to FIGS. 6-8 .Details of the voice signature network will be further described withreference to FIGS. 12 and 13 .

With continued reference to FIG. 3 , in some embodiments, ear-worndevice 302 may store the voice signatures of target speakers locally,such as in a memory 316 containing the voice signatures of one or morespeakers. In some embodiments, ear-worn device 302 may receive the voicesignature(s) of target speaker(s) from an external device, such aselectronic device 304. For example, ear-worn device 302 may beconfigured to communicate wirelessly with electronic device 304 in amulti-speaker environment, e.g., a multi-speaker conversation as shownin FIG. 1 . Ear-worn device 302 may be configured to transmit a segmentof an audio signal 332 detected from the microphone of the ear-worndevice to electronic device 304. Alternatively, a microphone of theelectronic device 304 may be configured to detect the audio signal 336from the conversation. The segment of the audio signal may be of alength, e.g., a few seconds to a few minutes, and includes speeches frommultiple speakers in the multi-speaker conversation, e.g., at thebeginning of the conversation.

Electronic device 304 may be configured to process the audio signal 336detected by the microphone of the ear-worn device (or the electronicdevice itself) to identify one or more speakers in the conversation. Insome embodiments, in identifying the one or more speakers in theconversation, electronic device 304 may be configured to execute a voicesignature network described above. The voice signature network may beconfigured to extract one or more voice signatures as output from inputaudio signal containing speech component(s) of one or more targetspeakers. Electronic device 304 may be configured to further identifyone or more speakers in the conversation based on the extracted voicesignature(s), against known speakers. In some embodiments, electronicdevice 304 may include a registry 320 of known speakers stored in amemory of the electronic device. Electronic device 304 may also includevoice signatures associated with known speakers pre-stored in thememory.

In some embodiments, electronic device 304 may identify one or morespeakers in the conversation by matching the extracted voicesignature(s) to the voice signatures of known speakers in the registry320. Electronic device 304 may automatically select target speaker(s)from the registry of known speakers whose signatures are matched to theextracted voice signature, assuming the target speakers are knownspeakers (of the wearer of the ear-worn device) that are speaking in theconversation. Additionally, and/or alternatively, electronic device 304may receive user selection(s) identifying the target speakers from theidentified speakers. For example, the user may select a subset of thepreviously identified speakers being matched and whom the wearer of theear-worn device prefers to listen. In some embodiments, withoutexecuting the voice signature network, the electronic device 304 maydisplay a list of known speakers in the registry and receives a userselection indicating a selection of one or more known speakers in theregister whom the wearer of the ear-worn device knows is/are present inthe conversation and prefers to listen to. In some other embodiments, ifan extracted voice signature does not match to any voice signaturesassociated with the registry of known speakers, electronic device 304may add a new speaker to the registry. The user may select that speakeras the target speaker, and along with additional other target speakers.

Subsequently, electronic device 304 may transmit the voice signature(s)of the selected target speaker(s) 334 to the ear-worn device 302. Insome examples, electronic device 304 may transmit the voice signature(s)of the selected target speaker(s) 334 to the ear-worn device 302.Alternatively, as described above, the ear-worn device 302 may store thevoice signatures of multiple speakers. In such case, electronic device304 may transmit identifier(s) of the selected target speaker(s) toear-worn device 302, which in turn can retrieve the stored voicesignature(s) of target speaker(s) based on the identifiers.

In some examples, electronic device 304 may also communicate (wired orwirelessly) with one or more servers 306, via a communication network,to cause the server(s) 306 to perform some of the operations describedabove. In a non-limiting example, electronic device 304 may communicatewith server 306 to cause server 306 to perform extraction of the voicesignature. In such case, the electronic device 304 may provide inputaudio signal of a conversation to the server 306. Server 306 may includea voice signature network, which may be triggered by the electronicdevice to extract voice signature(s) from the input audio signal.

FIG. 4A illustrates example components of an ear-worn device that may beconfigured to enhance speech of a target speaker in a multi-speakerenvironment, according to a non-limiting embodiment of the presentapplication. In some embodiments, ear-worn device 400 may be animplementation of at least a portion of the ear-worn device 108 of FIGS.1 and 2 and 300 of FIG. 3 . Ear-worn device 400 may include one or moremicrophones 402, one or more receivers 405, and a voice isolationnetwork 403 coupled in between the microphone(s) 402 and the receiver(s)405. In some embodiments, microphone(s) 402 may be configured to detectaudio signal. The audio signal may be generated by the microphone(s)from sound 401, e.g., speech in a conversation. In a multi-speakerconversation, the audio signal detected by the microphone(s) may includespeech components attributable to multiple speakers. In someembodiments, the audio signal detected by the microphone(s) may beanalog signal. The ear-worn device 400 may additionally include ananalog-to-digital converter (ADC, not shown) to convert the analogsignal to digital signal 406 as input to the voice isolation network403. In some embodiments, the microphone(s) 402 may be capable ofproducing digital audio signals. In such case, the audio signal detectedby the microphone(s) may be digital signal 406, which can be directlyprovided to the voice isolation network 403.

With further reference to FIG. 4A, voice isolation network 403 mayreceive the digital audio signal 406 and process the digital audiosignal 406 to output isolated speech 407. Receiver(s) 405 may beconfigured to output the isolated speech 407 for playback to the wearerof the ear-worn device. For example, the receiver(s) 405 may receive thedigital signal 407 from the voice isolation network and convert thedigital signal 407 to analog signal before producing the output signal409. The receiver 405 may be a speaker device (e.g., loudspeaker) insome embodiments. In other examples, the ear-worn device mayadditionally include a digital-to-analog converter (DAC, not shown) toconvert the digital signal 407 to analog signal as input to thereceiver(s) 405 for providing the output signal 409.

In some embodiments, ear-worn device 400 may include a digital signalprocessor (DSP, 404) coupled between the voice isolation network 403 andthe receiver(s) 405. The DSP 404 may be configured to process theisolated speech from the voice isolation network 403 and generate anenhanced output 408. For example, DSP 404 may include a frequency-basedamplification. In some embodiments, the isolated speech output from thevoice isolation network may include preferentially processed (e.g.,amplified or suppressed) speech components attributable totarget/non-target speakers. For example, the output from the voiceisolation network may include speech with increased signal-to-noiseratio (SNR) for the target speaker's speech, or the volume of one ormore target speaker(s) at a desirable level selected by the user (e.g.,the wearer of the ear-worn device). The details of preferentiallytreating the speech attributable to target/non-target speakers will befurther described in embodiments of a voice isolation network withreference to FIGS. 6A-6B.

FIG. 4B illustrates example components of a variation of the ear-worndevice in FIG. 4A that may be configured to enhance speech of a targetspeaker in a multi-speaker environment, according to a non-limitingembodiment of the present application. In some embodiments, ear-worndevice 470 may be an implementation of at least a portion of theear-worn device 108 of FIGS. 1 and 2 and 300 of FIG. 3 . Ear-worn device470 may have microphone(s) 420 to receive one or more audio input signal410 and receiver(s) 460, similar to microphone(s) 402 and receiver(s)405, respectively, described in FIG. 4A. The receivers 460 may processthe output signal 445 and output an output signal 490. The receivers 405and 460 may be considered an output module or output block in someembodiments. Ear-worn device 470 may also include voice isolationnetwork 450 similar to voice isolation network 403 of FIG. 4A, anddigital signal processor (DSP, 440) similar to DSP 404 of FIG. 4A.Additionally, ear-worn device 470 may include controller 430 configuredto control both the voice isolation network 450 and DSP 440.

Controller 430 receives digital audio signal 425. Controller 430 maycomprise one or more processor circuitries (herein, processors), memorycircuitries and other electronic and software components configured to,among others, (a) perform digital signal processing manipulationsnecessary to prepare the signal for processing by the voice isolationnetwork 450 or the DSP 440, and (b) to determine the next step in theprocessing chain from among several options. In one embodiment of thedisclosure, controller 430 executes a decision logic to determinewhether to advance signal processing through one or both of DSP 440 andvoice isolation network 450. For example, DSP 440 may be activated atall times, whereas controller 430 executes decision logic to determinewhether to activate the voice isolation network 450 or bypass the voiceisolation network by deactivating the voice isolation network 450. Insome embodiments, DSP 455 may be configured to apply a set of filters tothe incoming audio components. Each filter may isolate incoming signalsin a desired frequency range and apply a non-linear, time-varying gainto each filtered signal. The gain value may be set to achieve dynamicrange compression or may identify stationary background noise. DSP 440may then recombine the filtered and gained signals to provide an outputsignal 445.

The controller 430 may include storage circuitry 432 to store data, suchas data representing voices that, when detected, may serve as an inputto the controller's logic. For example, the storage circuitry 432 mayinclude a speaker registry of the types described herein, in thoseembodiments in which the voice registry is stored on the ear-worndevice.

As stated, in one embodiment, the controller performs digital signalprocessing operations to prepare the signal for processing by one orboth of DSP 440 and voice isolation network 450. Voice isolation network450 and DSP 440 may accept as input the signal in the time-frequencydomain (e.g., signal 425), so that controller 430 may take a Short-TimeFourier Transform (STFT) of the incoming signal before passing it ontoeither voice isolation network 450 or DSP 440. In another example,controller 430 may perform beamforming of signals received at differentmicrophones to enhance the audio signals coming from certain directions.

In certain embodiments, controller 430 continually determines the nextstep in the signal chain for processing the received audio data. Forexample, controller 430 activates voice isolation network 450 based onone or more of user-controlled criteria, user-agnostic criteria, userclinical criteria, accelerometer data, location information, stored dataand the computed metrics characterizing the acoustic environment, suchas SNR. For example, in response to a determination that the speech iscontinual, or that the SNR of the input audio signal is above athreshold ratio, controller 430 may activate the voice isolationnetwork. Otherwise, controller 430 may deactivate the voice isolationnetwork 450, leaving the DSP 440 activated. This results in a powersaving of the ear-worn device when the voice isolation network is notneeded. If voice isolation network 450 is not activated, controller 430instead passes signal 435 directly to DSP 440. In some embodiments,controller 430 may pass data to both voice isolation network 450 and DSP440 simultaneously as indicated by arrows from controller 430 to DSP 440and to voice isolation network 450.

In some embodiments, user-controlled criteria may represent one or morelogics (e.g., hardware- or software-implemented). In some examples,user-controlled criteria may comprise user inputs including theselection of an operating mode through an application on a user'ssmartphone or input on the ear-worn device (for example by the wearer ofthe ear-worn device tapping the device). For example, when a user is ata restaurant, she may change the operating mode to noisecancellation/speech isolation by making an appropriate selection on hersmartphone. Additionally, and/or alternatively, user-controlled criteriamay comprise a set of user-defined settings and preferences which may beeither input by the user through an application (app) or learned by thedevice over time. For example, user-controlled criteria may comprise auser's preferences around what sounds the wearer of the ear-worn devicehears (e.g., new parents may want to always amplify a baby's cry, or adog owner may want to always amplify barking) or the user's generaltolerance for background noise. Additionally, and/or alternatively, userclinical criteria may comprise a clinically relevant hearing profile,including, for example, the user's general degree of hearing loss andthe user's ability to comprehend speech in the presence of noise.

User-controlled logic may also be used in connection with or aside fromuser-agnostic criteria (or logic). User-agnostic logic may considervariables that are independent of the user. For example, theuser-agnostic logic may consider the hearing aid's available powerlevel, the time of day or the expected duration of the voice isolationnetwork operation (as a function of the anticipated voice isolationnetwork execution demands).

In some embodiments, acceleration data as captured on sensors in thedevice may be used by controller 430 in determining whether to directsignal controller output signal 435 to one or both of DSP 440 and voiceisolation network 450. Movement or acceleration information may be usedby controller 430 to determine whether the user is in motion orsedentary. Acceleration data may be used in conjunction with otherinformation or may be overwritten by other data. Similarly, data fromsensors capturing acceleration may be provided to the voice isolationnetwork as information for inference.

In other embodiments, the user's location may be used by controller 430to determine whether to engage one or both of DSP 440 and voiceisolation network 450. Certain locations may require activation of voiceisolation network 450. For example, if the user's location indicateshigh ambient noise (e.g., the user is strolling through a park or isattending a concert) and no direct conversation, controller 430 mayactivate DSP 440 only and deactivate voice isolation network 450. On theother hand, if the user's location suggests that the user is traveling(e.g., via car or train) and other indicators suggest humancommunication, then controller 430 may activate voice isolation network450 to enhance the audio signal by amplifying human voices over thesurrounding noise.

In some embodiments, controller 430 may execute an algorithmic logic toselect a processing path. For example, controller 430 may detect SNR ofinput audio signal 425 and determine whether one or both of DSP 440 andvoice isolation network 450 should be engaged. In one implementation,controller 430 compares the detected SNR value with a threshold valueand determines which processing path to initiate. The threshold valuemay be one or more of empirically determined, user-agnostic oruser-controlled. Controller 430 may also consider other user preferencesand parameters in determining the threshold value as discussed above.

In another embodiment, controller 430 may compute certain metrics tocharacterize the incoming audio as input for determining a subsequentprocessing path. These metrics may be computed based on the receivedaudio signal. For example, controller 430 may detect periods of silence,knowing that silence does not require the voice isolation network toenhance and it should therefore deactivate the voice isolation network.In another example, controller 430 may include a Voice Activity Detector(VAD) 434 to determine the processing path in a speech-isolation mode.In some embodiments, the VAD may be a compact (e.g., much lesscomputationally intensive) neural network in the controller.

In an exemplary embodiment, controller 430 may receive the output ofvoice isolation network 450 for recently processed audio, as indicatedby arrow from voice isolation network 450 to controller 430, as input tocontroller 430. Voice isolation network 450, which may be configured toisolate target audio in the presence of background noise, provides theinputs necessary to robustly estimate the SNR. Controller 430 may inturn use the output of the voice isolation network 450 to detect whenthe SNR of the incoming signal is high enough or too low to influencethe processing path. In still another example, the output of voiceisolation network 450 may be used to improve the robustness of VAD 434.Voice detection in the presence of noise is computationally intensive.By leveraging the output of voice isolation network 450, ear-worn device470 can implement this task with minimal computation overhead when thenoise is suppressed based on isolated speech from the voice isolationnetwork.

When controller 430 utilizes voice isolation network output 451, it canonly utilize the output to influence the signal path for subsequentlyreceived audio signal. When a given sample of audio signal is receivedat the controller, the output of voice isolation network 450 for thatsample will be computed with a delay, where the output of the voiceisolation network, if computed before the next sample arrives, willinfluence the controller decision for the next sample. When the timeinterval of the sample is small enough, e.g., a few milliseconds or lessthan a second, such delay will not be noticeable by the wearer.

When voice isolation network 450 is activated, using the output 451 ofthe voice isolation network 450 in the controller does not incur anyadditional computational cost. In certain embodiments, controller 430may engage voice isolation network 450 for supportive computation evenin a mode when voice isolation network 450 is not the selected signalpath. In such a mode, incoming audio signal is passed directly fromcontroller 430 to DSP 440 but data (i.e., audio clips) is additionallypassed at less frequent intervals to voice isolation network 450 forcomputation. This computation may provide an estimate of the SNR of thesurrounding environment or detect speech in the presence of noise insubstantially real time. In an exemplary implementation, controller 430may send a 16 ms window of data once every second for VAD 134 detectionat voice isolation network 150. In some embodiments, voice isolationnetwork 450 may be used for VAD 434 instead of controller 430. Inanother implementation, controller 430 may dynamically adjust theduration of the audio clip or the frequency of communicating the audioclip as a function of the estimated probability of useful computation.For example, if the audio signal (e.g., 425) exhibits a highly variableSNR, controller 430 may request additional voice isolation networkcomputation at more frequent intervals.

With reference to FIGS. 4A and 4B, ear-worn devices 400 and 470 may eachinclude a single ear-piece having a microphone. In other examples,ear-worn devices 400 and 470 may each be binaural and include twoear-pieces, each ear-piece having a respective microphone. Similarly,ear-worn devices 400 and 470 may each include one or more receiversrespectively included in one or two ear-pieces.

FIGS. 5A-5B illustrate variations of example components of an ear-worndevice having two microphones, according to a non-limiting embodiment ofthe present application. FIGS. 5A and 5B each includes a portion of acircuitry 500, 545 in an example ear-worn device, respectively. In someembodiments, the portions of circuitry 500, 545 may be implemented inear-worn device 108 (in FIGS. 1 and 2 ), 302 (in FIG. 3 ), 400 (in FIG.4A) and 470 (in FIG. 4B), where the ear-worn device is binaural. In FIG.5A, circuitry 500 may include a beamformer 530 configured to processaudio signal 519, 529 respectively detected from microphones 514 and 524(e.g., left and right microphones respectively residing in one of twoear-pieces of the ear-worn device and configured to receive input audiosignals 510 and 520, respectively). In some embodiments, beamformer 530may be implemented in controller 430 of FIG. 4B. Beamformer 530 maygenerate an enhanced audio signal 532 that accounts for sounds fromdifferent directions as detected by microphones 514 and 524. Asdescribed above, the audio signals 519, 529 respectively detected by themicrophones 514 and 524 may be digital signals. The output from thebeamformer 530 may be digital signal as well. As shown in FIG. 5A, theenhanced audio signal 532 may be provided to the voice isolation network540 in the ear-worn device. The voice isolation network 540 may besimilar to the voice isolation network described above, e.g., 403 inFIG. 4A and 450 in FIG. 4B. The output of the voice isolation networkmay be provided to the receivers of two ear-pieces.

In some embodiments, each ear-piece may be configured to communicatewith the other ear-piece and exchange audio signal with the otherear-piece. For example, beamformer 530 may be residing in a firstear-piece of an ear-worn device. The audio signal detected by themicrophone of the other ear-piece may be transferred from the otherear-piece to the ear-piece in which the beamformer 530 is residing. Theoutput of the voice isolation network 540, or the output of the DSP(e.g., 404 in FIG. 4A, 440 in FIG. 4B) may be transferred back to theother ear-piece. It is appreciated that the two ear-pieces may beconfigured to communicate using any suitable protocol, such asnear-field magnetic induction (NFMI) protocol, which allows for fastdata exchange over short distances. Further, beamformer 530 may beoptional, where a binaural audio stream may be detected from microphones514 and 524, and provided to the virtual isolation network 540 withoutusing a beamformer.

With reference to FIG. 5B, the circuitry 545 may include severalcomponents similar to those described in connection with FIG. 5A.Microphone 554 is the same as microphone 514 in some embodiments.Microphone 564 is the same as microphone 524 in some embodiments.Microphone 554 receives input audio signal 550 and microphone 564receives input audio signal 560. Microphone 554 outputs audio signal559, while microphone 564 outputs audio signal 569.

The circuitry 545 may include two separate voice isolation networks 570and 580 each residing in a respective ear-piece of the ear-worn device.In such case, each ear-piece of the ear-worn device may include anindependent configuration such as configurations described in 400 ofFIG. 4A) or 470 of FIG. 4B. The circuitry 545 may further include areconciler 590 configured to receive the output of the voice isolationnetwork or the output of the DSP (e.g., 404 of FIG. 4B or 440 of FIG.4B) from the two ear-pieces. The reconciler 590 may be configured toreconcile the outputs from the two ear-pieces and provide reconciledsignals to the two ear-pieces for playback. As described above, the twoear-pieces may be configured to communicate with each other. In someembodiments, the output of the voice isolation network or the DSP ofeach one ear-piece may be transferred to the other ear-piece forreconciliation by the reconciler 590, and the reconciled audio signalmay be transferred back from the other ear-piece. In some otherembodiments, each of the two ear-pieces may combine the output of thevoice isolation network from the other ear-piece with the output of itsown voice isolation network into a combined output and provide thecombined output for further audio processing. For example, the output ofthe voice isolation network in one ear-piece (e.g., a complex ratiomask) may be transmitted to the other ear-piece. The other ear-piece maycombine the received output with the output of its own voice isolationnetwork (e.g., taking the average of two complex ratio masks) forfurther audio processing (e.g., DSP).

FIGS. 6A and 6B illustrate an example configuration of a voice isolationnetwork, according to a non-limiting embodiment of the presentapplication. Voice isolation network 600 may be implemented as voiceisolation network 403 (in FIG. 4A), 450 (in FIG. 4B), 540 (in FIG. 5A)and 570, 580 (in FIG. 5B), in some examples, and may be implemented inear-worn device 108 (in FIGS. 1 and 2 ), 302 (in FIG. 3 ), 400 (in FIG.4A), and 470 (in FIG. 4B). Voice isolation network 600 may include avoice isolation machine learning (ML) model 602, which may be configuredto receive two inputs: audio signal 620 and voice signature(s) of targetspeaker(s) 622. The audio signal provided as input to the voiceisolation machine learning model may be audio signal detected frommicrophone(s) of an ear-worn device. The voice signature(s) of targetspeaker(s) may be obtained from an external electronic device associatedwith the wearer of the ear-worn device. The operations of the externalelectronic device that may be performed to provide voice signatures ofselected target speaker(s) for the wearer of the ear-worn device will befurther described in detail with reference to FIGS. 10-13 .

The voice isolation network 600 may additionally include one or morecomponents (e.g., relative gain filter 612, recombiner 614) to processthe isolated speech from the voice isolation machine learning model 602and preferentially treat (e.g., amplify or suppress) speech attributableto target/non-target speakers to produce an enhanced output audio signal626. The output audio signal 626 may be provided to a DSP (e.g., seeFIGS. 4A and 4B) for outputting to the receiver(s) of the ear-worndevice.

Returning to FIG. 6A, the input audio signal provided to the voiceisolation machine learning model 602 may include speeches of multiplespeakers (including target and non-target speakers) in a conversation.The audio signal may also include other signals, such as backgroundnoise. The voice signature(s) may include data (e.g., feature vectors)representative of selected target speakers in the multi-speakerconversation. In some examples, each voice signature feature vector(e.g., in frequency domain) may be a 256-dimensional embedding. Othersuitable dimensions may also be possible. With reference to FIG. 6B,voice isolation machine learning model 602 may include a combiner 604and a machine learning model core 606 configured to generate output 646.For example, output 646 may include masks 624. The combiner 604 may beconfigured to combine the input audio signal 620 and the voicesignatures 622 into a signal stream to be provided to the machinelearning model core 606.

FIG. 6C illustrates an example signal stream that may be concatenatedfrom an audio signal and voice signature(s) of target speaker(s) in thecombiner 604, according to a non-limiting embodiment of the presentapplication. As shown in FIG. 6C, a plurality of sequential segments maybe formed as inputs to the machine learning core 606, each including avector representing a respective audio segment from the input audiosignal appended by the voice signature(s) of one or more targetspeakers. A vector representing an audio segment from an input audiosignal may be the output of a STFT as previously described. For example,the system may convert the audio segment into time-frequency domainvector by taking an STFT of the signal as previously described. Asdescribed above, a voice signature may also be a vector, e.g., amulti-dimensional feature vector. The STFT operation may be performedinside the voice isolation network 602 (e.g., inside combiner 604, orinside another component in the voice isolation network, now shown), oroutside the voice isolation network 602. In the latter case, a componentin the ear-worn device may convert the audio signal detected by themicrophone(s) into vectors representing audio segments in the audiosignal and provide the vectors (instead of audio signal) to the voiceisolation network 602. As shown in FIG. 6C, the audio segment vectors inthe inputs may be representative of sequential segments in the audiosignal. For example, an audio signal may be segmented into multiplesequential segments, each converted to a vector as described above, suchas seg1, seg2, seg3, etc., where each of the sequential segments mayinclude an audio signal frame within a small time period. In someexamples, an audio signal frame may have a length of 1 ms, 2 ms, 3 ms, 4ms, or 5 ms. In other examples, the audio signal frame may have a lengthof greater than 30 ms (and less than one second), or any suitable timeperiod. In a multi-speaker conversation, each of the target speakers maybe associated with a distinctive voice signature. For example, vs1, vs2,vs3 may respectively represent voice signatures of target speaker 1,target speaker 2, and target speaker 3.

An example of a concatenated signal stream is shown in FIG. 6C. Forexample, if two target speakers are selected, the first input 632 to themachine learning core 606 may include vector seg1 representing a firstaudio segment appended by two voice signature feature vectors vs1 andvs2. The second input 634 to the machine learning core 606 may includevector seg2 representing a second audio segment appended by two voicesignature feature vectors vs1 and vs2. The third input 636 to themachine learning model core 606 may include vector seg3 representing athird audio segment appended by two voice signature feature vectors vs1and vs2. Addition inputs to the machine learning model core 606 may takethe same, or a similar, format. Using the voice signatures in theoperation of the voice isolation machine learning model enables themachine learning model, when properly trained, to isolate the speech oftarget speaker(s) from non-target speaker(s) in the audio signal. Thetraining of the voice isolation machine learning model will be furtherdescribed with reference to FIG. 8 .

Returning to FIG. 6A, the voice isolation machine learning model 602 maybe a recurrent neural network, a convolutional neural network or anyother suitable neural network. The output of the voice isolation machinelearning model may include a mask 624 for isolating speeches from targetspeaker(s). In operation, a source separator 608 may be configured toapply the mask 624 outputted from the voice isolation machine learningmodel 602 to the input audio signal to provide isolated speech 610. Asshown in FIG. 6A, isolated speech 610 may include a plurality of speechcomponents each corresponding to a class of sounds or an individualspeaker. For example, the isolated speech may include speech componentsof target speaker 1, target speaker 2, and non-target speaker(s) orbackground noise.

In some embodiments, the voice isolation network may be configured toisolate speech of any suitable number of multiple target speakers withproper training datasets. For example, a voice isolation network may beconfigured to handle a dynamically changing number of target speakers.For example, the voice isolation network may be configured to isolatespeech of up to four target speakers, including 0, 1, 2, 3, and 4 targetspeakers. In implementing this, the network may be configured to havethe size of input and output for four target speakers, where the inputand output may contain multiple zeros where there are fewer than fourtarget speakers. In training the network, various training datasets maybe provided for various scenarios including speech of up to fourdistinct speakers. In such a configuration, the input to the voiceisolation network (e.g., see FIG. 6C) may include a vector representingan audio segment appended by four feature vectors for four voicesignatures, where some of the feature vectors may be zero when there arefewer than four target speakers. Additionally, and/or alternatively, thevoice isolation network may be configured to isolate multiple targetspeakers by taking a combined voice signature from those of multipletarget speakers. For example, the voice signatures of multiple targetspeakers may be combined by averaging them, where the averaged voicesignature may be provided to the voice isolation network. In trainingthe network, the voice signatures from multiple speakers may be combinedin a similar manner before being provided to the voice isolation networkbeing trained.

In a non-limiting example, a mask 624 outputted by the voice isolationmachine learning model 602 may include complex values. When the mask isapplied to the input audio signal, the magnitude and phase of the inputaudio signal are modified to yield the output signal stream includingthe isolated speech from the target speaker(s). In some embodiments, amask outputted by the voice isolation machine learning model may bespecific to an individual target speaker or multiple target speakers.Thus, the isolated speech 610 may include signals including speeches ofone or more target speaker(s). Subsequently, the signals attributable tonon-target speaker(s) and/or noise may be obtained by subtractingspeeches attributable to the target source(s) from the input audiosignal.

With continued reference to FIG. 6A, voice isolation network 600 mayinclude additional components to further process the isolated speech toprovide enhanced speech with the speech of the target speaker(s) beingpreferentially treated. As described above, the isolated speech 610 mayinclude isolate speeches corresponding to different sound sources, forexample, different target speakers and non-target speakers or noise. Theear-worn device may provide the isolated speech as different bands to arelative gain filter 612, which applies different gains based on userpreferences 616. As described above, user preferences 616 may containinformation about the optimal combination (or optimal weights) ofvarious sound sources. Recombiner 614 then combines the differentiallyweighted frequency bands to form a combined output audio signal.

Referring again to FIGS. 4A and 4B, voice isolation network 600 directsthe recombined audio stream to DSP 404 or 440 for further processing. Inthis manner and according to one embodiment, voice isolation network 600estimates an ideal ratio mask that separates speech signal from noisesignal, applies differential gain to each of the identified speech andnoise signals and combines the differentially amplified signals into onedata stream.

In some embodiments, voice isolation network 600 may optionally includea performance monitor 618. Performance monitor 618 may be configured toreceive output of the voice isolation machine learning model 602 topredict the performance or predict the error of the voice isolationmachine learning model. These predictions can further be used as inputsin recombiner 614, which seeks to optimize the way in which modeloutputs are recombined to form a final signal. Recombiner 614 takes intoaccount both the user preferences 616 and output of performance monitor618 to optimally recombine the outputs of isolated speech from the voiceisolation machine learning model 602.

In an exemplary embodiment, performance monitor 618 receives outputsignal from the voice isolation machine learning model in sequentialframes and determines an SNR for each frame. Performance monitor 618then estimates an average SNR for the environment, which can be used topredict model error (since model error typically increases at morechallenging input SNRs). Recombiner 614 also receives user preferences616. Given the user preferences 616 and the estimated SNR from theperformance monitor 618, recombiner 614 then determines a set ofrelative gains for the relative gain filter 612 to be applied to theisolated speech from the voice isolation machine learning model. In anexemplary embodiment, the recombiner 614 seeks to set the gains to bestmatch user preferences while keeping total error below a certainthreshold.

In some embodiments, recombiner 614 applies the gain values to theisolated speech to obtain output audio signal. In one embodiment, aplurality of gain values is communicated to recombiner 614. Each gainvalues corresponds to an intermediate signal, which in turn correspondsto a sound source. Recombiner 614 multiplies each gain value to itscorresponding intermediate signal and combines the results to produceoutput audio signal. In some embodiments, the output audio signal fromthe recombiner 614 may be provided to a DSP for further processing, asshown in FIGS. 4A and 4B.

Returning to FIG. 6A, relative gain filter 612 may receive the user'sauditory preferences from user preferences 616 and apply one or morerelative gains to each of the frames of isolated speech signal. In someembodiments, the gains applied to the different frequency bands in theisolated speech can be non-linear. The implementation allows differentgains to be applied at the source and at per-frame level. In anon-limiting example, the relative gains may be set to cause therelative gain filter to increase the SNR of the target speaker(s). Forexample, a higher gain may be applied to the speech componentattributable to the target speaker(s) and a lower gain may be applied tothe speech component attributable to non-target speaker(s) or backgroundnoise. Alternatively, the relative gains may be set to cause therelative gain filter to set the volume of the speech of the targetspeaker(s) to be at a desirable level. For example, the desirable levelfor a target speaker may be configured in the user preferences 616. Inother variations, the volume of each target speaker may be equalizedamong the multiple target speakers. In some embodiments, the volume ofeach target speaker may be adjusted independently depending on how farthe speaker is from the wearer of the ear-worn device. Adjusting thevolumes of the target speaker(s) may include applying a higher gain tothe speaker sitting farther away so that the volume of the voice at thereceiver(s) of the ear-worn device is the same as that of the voice ofthe person sitting closer. In some embodiments, information indicativeof the distance from a speaker to the wearer may be provided to theear-worn device via an application on the external electronic deviceassociated with the wearer. In some embodiments, the ear-worn device mayadjust the volumes of speakers based on the control of the wearer (e.g.,via an application on the external electronic device).

In some embodiments, the relative gains may be set to cause the relativegain filter to attenuate speech of non-target speaker(s) and/or thebackground noise. Additionally, and/or alternatively, the ear-worndevice may receive an indication (e.g., set on the phone associated withthe wearer of the ear-worn device, or set on the ear-worn device with auser selection) to suppress the wearer's own speech. In suchconfiguration, the wearer him/herself may be designated as a non-targetspeaker whose speech should be isolated by the voice isolation networkin the manner described herein for target speakers, but whose speechshould be de-emphasized or suppressed. Once the wearer's own speech isisolated, the ear-worn device may set the relative gains toattenuate/suppress the wearer's speech. As the result, the output signalmay be enhanced to include only speech of target speaker(s) other thanthe wearer of the ear-worn device. It is appreciated that the voiceisolation network 600 may be implemented in configuration as describedin FIG. 4B, in which the voice isolation network may be activated ordeactivated by a controller (e.g., 430) as described above.

FIG. 7A is a flowchart of an example method 700 of operation of anear-worn device configured to selectively isolate speech from a targetspeaker within a multi-speaker environment, according to a non-limitingembodiment of the present application. In some embodiments, method 700may be implemented by a processor in an ear-worn device such as 108 (inFIGS. 1 and 2 ), 302 (in FIG. 3 ), 400 (in FIG. 4A), 470 (in FIG. 4B),500 (in FIG. 5A) or 545 (in FIG. 5B).

Method 700 may implement any of the operations in various embodimentsdescribed above. For example, method 700 may include detecting an audiosignal with a microphone of an ear-worn device at act 702, providing theaudio signal detected by the microphone of the ear-worn device to theprocessor of the ear-worn device at act 704; and isolating, with theprocessor of the ear-worn device, speech of target speaker(s) with amachine learning model using voice signature(s) of the targetspeaker(s), at act 708. In some embodiments, the machine learning modelmay be a voice isolation machine learning model in a voice isolationnetwork, such as voice isolation network described above in FIGS. 6A-6B.The machine learning model 602 may receive two inputs: the audio signaldetected by the microphone of the ear-worn device and voice signature(s)of the target speaker(s). Voice signature of a speaker, as previouslydescribed, may be a unique representation of the speaker thatdifferentiates the voice of the speaker from those of other speakers.Using the voice signature of target speaker(s) as input, the machinelearning model may be operated to isolate the speech from the targetspeaker(s) in the audio signal. The isolated speech may include acomponent of the audio signal representing the target speaker's speechfrom among the temporally overlapping speech components from multiplespeakers.

In some embodiments, the voice signature(s) of the target speaker(s) maybe obtained from another machine learning model trained to discriminatebetween voices of speakers. With reference to FIG. 7A, method 700 mayoptionally include receiving voice signature(s) of target speaker(s) atact 706. For example, as shown in FIG. 3 , the ear-worn device 302 maywirelessly receive the voice signature(s) of target speaker(s) from anelectronic device 304, such as a phone associated with the wearer of theear-worn device. In some embodiments, the ear-worn device 302 may beconfigured to send a segment of an audio signal detected from themicrophone of the ear-worn device to electronic device 304, which mayprocess the audio signal to extract voice signature(s) of the targetspeaker(s). Accordingly, act 706 may further include sending a segmentof an audio signal to an external electronic device in the manner asdescribed in embodiments of FIG. 3 before receiving the voicesignature(s) of the target speaker(s).

FIG. 7B illustrates a flowchart of an example method 750 as a variationof the example method in FIG. 7A of operation of an ear-worn deviceconfigured to selectively isolate speech from a target speaker within amulti-speaker environment, according to a non-limiting embodiment of thepresent application. Similar to method 700 in FIG. 7A, method 750 may beimplemented by a processor in an ear-worn device such as 108 (in FIGS. 1and 2 ), 302 (in FIG. 3 ), 400 (in FIG. 4A), 470 (in FIG. 4B), 500 (inFIG. 5A) or 545 (in FIG. 5B). In some embodiments, method 750 mayinclude detecting audio signal with a microphone of an ear-worn deviceat 752 and providing the audio signal to the processor of the ear-worndevice at act 754, where acts 752 and 754 may be respectively performedin a similar manner as acts 702 and 704 in method 700.

Additionally, and/or alternatively, method 750 may include increasingSNR of the target speaker(s) with a machine learning model using voicesignature(s) of the target speaker(s), at act 758. Similar to method700, the machine learning model may be a voice isolation machinelearning model in a voice isolation network, such as voice isolationnetwork described above in FIGS. 6A-6B. For example, the machinelearning model may receive two inputs: the audio signal detected by themicrophone of the ear-worn device and voice signature(s) of the targetspeaker(s), and output isolated speech from the target speaker(s).Further, act 758 may include one or more operations that may beperformed in the voice isolation network as described in FIG. 6B. Forexample, recombiner 614 may be operated to output audio signal includingimproved SNR of the target speaker(s).

At act 756, similar to act 706 of method 700, the voice signature oftarget speaker(s) to be used with the machine learning model may bereceived (e.g., wirelessly) from an external electronic device. Further,act 756 may include sending a segment of an audio signal to an externalelectronic device in the manner as described in embodiments of FIG. 3before receiving the voice signature(s) of the target speaker(s).

In both acts 706 and 756, in some embodiments, the voice signatures ofmultiple speakers are stored in the external electronic device, andthus, the voice signature(s) of the target speaker(s) are received fromthe external electronic device. In other embodiments, voice signaturesof multiple speakers may be stored in the ear-worn device. As such, theidentifiers that identify the target speaker(s) are received by theear-worn device, which may use the identifiers to retrieve thecorresponding voice signatures of the target speaker(s) from the localstorage of the ear-worn device. In some other embodiments, rather thanreceiving voice signature(s) from an external device, acts 706/756 mayinclude generating the voice signature(s) by the ear-worn device itselfusing a voice signature network that is also residing in the ear-worndevice. In some other embodiments, voice signature(s) representingdefault target speakers may be pre-stored on the ear-worn device. Insuch configuration, acts 708/758 may include retrieving the voicesignature(s) from a memory location of the ear-worn device and providingthe voice signature(s) to the machine learning model.

In some embodiments, the target speaker(s) may be maintained the sameduring a conversation session, e.g., a meeting, a diner, in which theparticipants in the conversation do not change. In such case, the voicesignature(s) of the target speaker(s) may be selected at the beginningof the conversation and remain constant throughout the conversationsession. Thus, acts 708/758 (of method 700/750) may include providingthe voice signature as a constant input to the machine learning modelduring processing of the audio signal. Alternatively, the model may beconfigured such that it continues to target those voice signaturespreviously provided, until it receives a new voice signature or aninstruction indicating no voice signature. In such configurations, themachine learning model may be run in an efficient manner, obviating theneed to keep passing large inputs to the network. In some embodiments,an indication of no voice signature may include a voice signature beingset to a default value (e.g., all zeros in a voice signature). In someembodiments, an indication of no voice signature may cause the machinelearning model to operate to isolate all voices.

In some embodiments, the target speaker(s) may change during aconversation. This may happen when the participants in the conversationchange (e.g., a speaker left the conversation, or a new speaker joined),or when the wearer of the ear-worn device decides to listen to adifferent target speaker. In some embodiments, the wearer of theear-worn device may be a user of his/her phone during a conversation, asshown in FIG. 1 . Thus, the user may change target speaker(s) by makinga user selection on the phone. In other embodiments, the ear-worn devicemay periodically transmit an audio signal segment detected by themicrophone(s) to the external electronic device, which generates andsends updated voice signature(s) of target speaker(s) to the ear-worndevice. At such time when the target speakers are updated, acts 706/756(of method 700/750) may be triggered to receive updated voicesignature(s) of the target speaker(s). Consequently, acts 708/758 (ofmethod 700/750) may include providing a second voice signature as inputto the machine learning model in place of the previous voice signatureduring processing of the audio signal.

It is appreciated that each of methods 700 and 750 may include one ormore additional acts to implement one or more operations described aboveto enhance the audio signal. For example, by the DSP (404 of FIG. 4A or440 of FIG. 4B), the ear-worn device may play out only the targetspeaker's speech after isolating the component of the audio signalrepresenting the target speaker's speech. In implementing this, methods700 or 750 may amplify the component of the audio signal representingthe target speaker's speech and/or apply a lower gain to or not amplifythe speech component attributable to the non-target speaker(s) orbackground noise. Alternatively, and/or additionally, method 700 or 750may include executing the recombiner 614 of FIG. 6A to suppress thecomponent of the audio signal representing a non-target speaker's speechafter isolating the component of the audio signal representing thetarget speaker's speech.

FIG. 8 is a block diagram illustrating at 800 the training and deployingof a voice isolation machine learning model for isolating speech from atarget speaker, according to a non-limiting embodiment of the presentapplication. In some embodiments, a voice isolation machine learningmodel training system 802 may be configured to use training dataset 804to generate a trained voice isolation machine learning model 806. Thetrained voice isolation machine learning model 806 may be deployed asthe voice isolation machine learning model 602 in the voice isolationnetwork 600, for example. The train voice isolation machine learningmodel 806 may also be implemented in voice isolation network describedabove, such as 403 (in FIG. 4A), 450 (in FIG. 4B), 540 (in FIG. 5A), 570and 580 (in FIG. 5B). In some embodiments, the voice isolation machinelearning model 806 may include a neural network comprising a pluralityof layers, each having multiple weights. It is appreciated that thevoice isolation machine learning model 806 may be any suitable modelconfigured in a suitable manner. In a non-limiting example, the machinelearning model 806 may be a recurrent neural network and may be a longshort term memory (LSTM) network. In some examples, the machine learningmodel 806 may include five LSTM layers, each layer having a number ofunits, e.g., 1024 or other suitable number of units. The number ofweights in the machine learning model 806 may be as few as thousands toas large as tens of millions. Training system 802 may use trainingdataset 804 to train the weights in the machine learning model 806.

In some embodiments, training dataset 804 may include clips of cleanspeech 810 and noisy speech 812 for a plurality of speakers. Forexample, for each of the plurality of speakers, the training dataset 804may include a plurality of pairs of clips containing speech from thespeaker. Each pair of clips may include a clip containing clean speechand another clip containing the same clean speech with interfering noiseadded (thus noisy speech). The interfering noise may include randomlyselected background noise and also interfering contemporaneous speech ofa non-target speaker or other speakers. The data can also be augmentedby adding reverberation, in some examples. Thus, training system 802receives the plurality of pairs of clips for the plurality of speakersas input. Additionally, training system 802 may also receive voicesignatures of the plurality of speakers as a third input.

In training the voice isolation machine learning model 806, apre-trained voice isolation machine learning model is initialized andcontains initial weights. The training system then provides the noisyspeech and the voice signatures 824 to the pre-trained voice isolationmachine learning model to generate an output for each data point in eachof a plurality of iterations in an optimization process. For example,each data point in an iteration may be a small segment of the noisyspeech (e.g., a chunk of one second) of a speaker combined with thevoice signature for the speaker. The clips of clean speech may be usedas ground truth data. In some embodiments, the segment of the noisyspeech and the voice signature of the speaker may be concatenated in thesame manner as described in FIG. 6C as used in executing the voiceisolation network (or voice isolation machine learning model). In someembodiments, the output of the voice isolation machine learning modelfor each data point may be an estimated complex mask for the noisy clip.A loss function is calculated based on the difference (e.g., measured bymean-squared error) between the ground truth complex mask and theestimated complex mask. The objective of the training is to minimizesuch loss function through multiple iterations in the optimizationprocess. Any suitable algorithm, such as gradient descent, may be usedin the optimization process. Once the training is completed, the weightsin the voice isolation machine learning are trained.

Voice signatures 824 provided to the training system 802 may bepre-stored or generated concurrently with the training. In someembodiments, voice signatures of plurality of speakers may be extractedfrom audio signals containing speeches of the speakers using a voicesignature network, which will be described in detail with reference toFIGS. 12-13 . The generated voice signatures may be pre-stored, e.g.,together with the training system. In other embodiments, trainingdataset 804 may include additional clips of clean speech 822 for theplurality of speakers, which are provided to a trained voice signaturemachine learning model 808. The trained voice signature machine learningmodel 808 is then executed to generate the voice signature for eachadditional clip. As such, the training dataset 804 includes a triplet ofclips for each speaker, including two clips of clean speech and a clipof noisy speech. In this configuration, one clip of clean speech is usedas ground truth, the other clip of clean speech is used to generate avoice signature. The generated voice signature and the clip of noisyspeech are then provided to the training system 802.

The trained voice isolation machine learning model 806 may be deployed(executed) in the voice isolation network described in variousembodiments above. The trained voice isolation machine learning modelmay take any new noisy speech 814 along with the voice signature(s) ofthe target speaker(s) 816 and generate results 818 (e.g., complex masksas described above). The new noisy speech and the voice signature(s) oftarget speaker(s) may be combined in a similar manner as described inFIG. 6C, and such manner of combination will therefore not be repeatedherein.

FIG. 9 illustrates a block diagram of a system-on-chip (SOC) packagethat may be implemented in an ear-worn device, according to anon-limiting embodiment of the present application. In some embodiments,SOC package 902 may implement various operations in an ear-worn device,such as 108 (in FIGS. 1 and 2 ), 302 (in FIG. 3 ), 400 (in FIG. 4A), 470(in FIG. 4B), or a circuitry of an ear-worn device such as 500 (in FIG.5A), or 545 (in FIG. 5B). In various embodiments, SOC 902 includes oneor more Central Processing Unit (CPU) cores 920, an Input/Output (I/O)interface 940, and a memory controller 942. Various components of theSOC package 902 may be optionally coupled to an interconnect or bus suchas discussed herein with reference to the other figures. Also, the SOCpackage 702 may include components such as those discussed withreference to the ear-worn device described in FIGS. 1-8 . Further, eachcomponent of the SOC package 920 may include one or more othercomponents of the ear-worn device, e.g., as discussed with reference toFIGS. 4A-6B. In one embodiment, SOC package 902 (and its components) isprovided on one or more Integrated Circuit (IC) die, e.g., which arepackaged into a single semiconductor device. The single semiconductordevice may be configured to be used as an ear-worn device, anamplification system or a hearing device to be used in the human earcanal.

As illustrated in FIG. 9 , SOC package 902 is coupled to a memory 960via the memory controller 942. In an embodiment, the memory 960 (or aportion of it) can be integrated on the SOC package 902. The I/Ointerface 940 may be coupled to one or more I/O devices 970, e.g., viaan interconnect and/or bus such as discussed herein. I/O device(s) 970may include interfaces to communicate with SOC 902. In an exemplaryembodiment, I/O interface 940 communicates wirelessly with I/O device970. SOC package 902 may comprise hardware, software and logic toimplement, for example, the various components or methods described inFIGS. 1-8 . The implementation may be communicated with an auxiliarydevice, e.g., I/O device 970. I/O device 970 may comprise additionalcommunication capabilities, e.g., cellular, BlueTooth, WiFi or otherprotocols, to access any component in the ear-worn device, for example,to configure the voice isolation network.

FIG. 10 is a block diagram illustrating a portion of a circuitryconfiguration of an electronic device operable to extract voicesignature of target speaker(s) to an ear-worn device, according to anon-limiting embodiment of the present application. In some embodiments,the wearer of an ear-worn device may be a user of his/her phone during aconversation, as shown in FIG. 1 , and use his/her phone to performvarious operations in association with voice processing on the ear-worndevice. For example, circuitry 1000 may be implemented in electronicdevice 110 (in FIGS. 1 and 2 ), and 304 (in FIG. 3 ) to provide thevoice signature(s) for target speaker(s) 1018 and transmit (e.g., wiredor wirelessly) the voice signature(s) to the ear-worn device (see FIG. 3). Circuitry 1000 may include various components, either hardware- orsoftware-implemented. In some embodiments, circuitry 1000 may include avoice signature network 1002 described above. The voice signaturenetwork 1002 may be configured to receive an audio signal 1014 as inputand extract as output voice signature of the speaker(s) 1016 whosespeech is included in the audio signal. In some embodiments, the voicesignature network may include a voice signature machine learning modelthat can be trained. When properly trained, the voice signature machinelearning model may be executed to extract from input audio signaldistinctive voice signatures for different speakers and extract the samevoice signature for the same speaker. A voice signature may be in theform previously described, e.g., in a multi-dimensional feature vector.The training of the voice signature machine learning model is describedwith reference to FIG. 13 .

With further reference to FIG. 10 , circuitry 1000 may include a targetspeaker selector 1004, a user interface 1006 coupled to the targetspeaker selector 1004. Circuitry 1000 may further include a speakerregistry 1008 to store a list of known speakers to the wearer of theear-worn device, and storage 1010 to store voice signatures of speakers.In some embodiments, the voice signatures 1010 of known speakers may bestored in association with registry 1008 of known speakers, where eachentry of the registry may correspond to a respective individual speaker.As shown in FIG. 10 , registry 1008 and voice signatures 1010 may bestored on the electronic device. In other embodiments, the voicesignatures may be optionally and/or additionally stored on the ear-worndevice associated with the electronic device. In some embodiments,circuitry 1000 may include a voice signature collector 1012, which isconfigured to collect the voice signatures 1010. The collection of voicesignatures will be further described with reference to FIG. 12 .

As shown in FIG. 10 , target speaker selector 1004 may be configured toselect target speaker(s) for the user and transmit the voicesignature(s) of selected target speaker(s) to the user's ear-worndevice. The operations of the target speaker selector 1004 are furtherexplained with examples in FIGS. 11A-11F. FIGS. 11A, 11C, and 11Eillustrate examples of graphical user interface that may be implementedin an electronic device, according to some non-limiting embodiments ofthe present application. FIGS. 11B, 11D, and 11F illustrate blockdiagrams of example processes respectively for implementing the examplegraphical user interfaces shown in FIGS. 11A, 11C, and 11E.

With reference to FIG. 11A, an example display 1100 may be implementedin user interface 1006 of FIG. 10 . The display of the user interface1100 may include a list 1102 of known speakers to user (e.g., the wearerof the ear-worn device). The known speakers may be registered in thespeaker register (e.g., 1008 in FIG. 10 ). The user may view the listedspeakers and confirm who is present in a conversation and/or whom theuser would like to listen to. This may be implemented by the userproviding a user selection of target speaker(s) from the list ofspeakers in the registry. As shown in the display 1100, each speakername may be displayed with a check box 1106 for the user toselect/unselect. Once the user has made the selection, the user mayclick a “Send” button 1104 to send the voice signature(s) of selectedtarget speaker(s) to the ear-worn device. In some embodiments, the usermay not need to click a “Send” button. Instead, once the target speakersare selected/updated (automatically, or by the user), the electronicdevice may dynamically send the voice signature(s) of the updated targetspeaker(s) to the ear-worn device.

FIG. 11B illustrates an example process 1120 for implementing theexample graphical user interface of FIG. 11A. In some embodiments,method 1120 may be implemented in an electronic device, e.g., 110 (inFIGS. 1 and 2 ), 304 (in FIG. 3 ). For example, method 1120 may beimplemented in the target speaker selector 1104 of FIG. 10 . In someembodiments, method 1120 may include displaying identities of knownspeaker(s) in a registry at act 1122. For example, as shown in FIG. 11A,the user interface may display a list 1102 of known speakers in theregistry. Method 1120 may further include receiving user selectionidentifying the target speaker(s) at act 1124. Although it is shown inFIG. 11A that the user selection may be click(s) 1106, the userselection may also take other forms, such as drop-down menu or othersuitable widgets.

In response to receiving the user section at act 1124, method 1120 mayproceed to determine whether there is at least one target speakerselected, at act 1126. In response to determining that at least onetarget speaker is selected, method 1120 may proceed to transmit thevoice signature(s) associated with the selected speaker(s) to theear-worn device, at act 1128. For example, the method may enable theuser to click “Send” button 1104 of FIG. 11A to transmit the voicesignature(s). In another example, the method may dynamically transmitthe voice signature(s) once the selected speaker(s) are updated, withoutrequiring the user to click a “Send” button. In response to determiningthat no target speaker is selected, or no user selection is received,method 1120 may stop. In some embodiments, in response to determiningthat no target speaker is selected, or no user selection is received,method 1120 may disable the “Send” button 1104 of FIG. 11A (e.g., the“Send” button may be grayed out), in which case, the electronic devicewill not transmit any voice signature to the ear-worn device.

With reference to FIG. 11C, an example display 1140 may be implementedin user interface 1006 of FIG. 10 . The display 1140 may include a list1102 of one or more identified speakers. In some embodiments, theelectronic device may receive audio signal detected by the microphone ofthe ear-worn device (or the electronic device itself) in a conversationto identify one or more speakers in the conversation. A speaker may beidentified when an extracted voice signature from the audio signal ismatched to a voice signature associated with a registry of knownspeakers. Extracting voice signature(s) from an audio signal will befurther described in FIG. 11D. In some embodiments, the electronicdevice may transmit the voice signature(s) of the identified one or morespeakers to the ear-worn device, assuming, by default, any knownspeakers in the registry who are also present in a conversation (thus,the voice signature is matched, and the speaker is identified) aretarget speakers to whom the wearer of the ear-worn device prefers tolisten. Additionally, in the user interface shown in 1140, the user maybe prompted to select, at 1146, a subset of the identified speakers tofurther confirm the target speaker(s). Then, the user may proceed totransmit the voice signatures of the selected target speaker(s), e.g.,by clicking the “Send” button 1144.

Additionally, and/or alternatively, the list 1142 may include one ormore un-identified speakers who voice signature(s) are not matched toany of the voice signatures of known speakers in the registry. Anun-identified speaker may be a new speaker whom the wearer of theear-worn device has never spoken with before. In some embodiments, theuser interface may allow the user to add a new un-identified speaker tothe registry, e.g., by clicking “Update registry” button 1150. Once theuser selects an un-identified speaker to be added to the registry, theun-identified speaker may be added to the registry. The newly addedspeaker may also be automatically selected as a target speaker.

Additionally, and/or alternatively, the user interface 1140 may includea user selection 1148 that, when selected, enables the user to suppresshis/her own voice. Such user selection may be an indication forsuppressing the wearer's own voice, where the indication can betransmitted from the electronic device to the ear-worn device.Alternatively, the ear-worn device may include a user interface (e.g.,by user clicking one or more buttons or a combination of buttons) toconfigure the ear-worn device to suppress the wearer's own voice. In analternative embodiment, the ear-worn device may be configured, bydefault, to suppress the wearer's own voice. The techniques forsuppressing the wearer's own voice are previously described withreference to FIG. 6A, and the description of those techniques will notbe repeated herein.

FIG. 11D illustrates an example process 1160 for implementing theexample graphical user interface of FIG. 11C. In some embodiments,method 1160 may be implemented in an electronic device, e.g., 110 (inFIGS. 1 and 2 ), 304 (in FIG. 3 ). For example, method 1160 may beimplemented in the target speaker selector 1104 of FIG. 10 . In someembodiments, method 1160 may include receiving input speech signal atact 1162. As previously described, the electronic device may receive theinput speech signal from an ear-worn device. For example, the inputspeech signal may be detected by the microphone(s) of the ear-worndevice and transmitted wirelessly to the electronic device.Alternatively, or additionally, the electronic device may receive theinput speech signal from its own microphone(s), as described in FIG. 3(showing the audio signal to be processed in the electronic device maybe come from either the ear-worn device and/or the electronic deviceitself). As described above, the input speech signal may include thespeech from one or more target speakers. The input speech signal mayalso include the speech from one or more non-target speakers. In someexamples, the input speech signal may be detected in a beginning of amulti-speaker conversation, during which period every speaker (or everytarget speaker) has spoken at least once.

With further reference to FIG. 11D, method 1160 may include extractingvoice signature(s) of speakers from the input speech signal using avoice signature network at act 1164. The voice signature network may beconfigured as described previously as in 1002 of FIG. 10 , for example.Using the voice signature network, act 1164 may extract the voices ofthe speakers in the conversation based on the input speech signal.Method 1160 may further match the extracted voice signatures, at act1166, with the voice signatures (e.g., 1010 of FIG. 10 ) associated withthe known speakers in the registry (e.g., 1008 of FIG. 10 ). If anextracted voice signature is matched to a voice signature associatedwith a known speaker, method 1160 may identify that known speaker.Method 1160 may further display a respective entry of the identifiedspeaker in the registry at act 1168. For example, list 1142 in the userinterface 1140 displays the names of the speakers in the registry whosevoice signatures respectively match an extracted voice signature fromthe input speech signal.

In some embodiments, method 1160 may handle an unidentified speaker. Anexample of an “un-identified” entry is shown in list 1142 of FIG. 11C.Method 1160 may determine whether an extracted voice signatures from theinput speech signal is not matched to any of the voice signatures of theknown speakers in the registry at 1170. In response to determining anunmatched voice signature, method 1160 may proceed to enable the user ofthe electronic device to update the registry of speakers at act 1172,for example, by clicking “Update registry” button 1150 of FIG. 11C.Updating the registry will further be described in FIGS. 11E and 11F.

With continued reference to FIG. 11D, in response to determining nounmatched voice signatures, method 1160 may optionally receive userselection identifying target speaker(s) from the matched speakers at act1174. Before receiving user selection identifying the target speaker(s),act 1174 may display the user with an option to select the targetspeaker. For example, a list of selection boxes 1146 of FIG. 11C may bedisplayed aside the list of identified speakers 1142. Each of theselection boxes 1146 of FIG. 11C may be clickable to allow the user toclick/unclick a corresponding speaker in the list. Thus, the selectedtarget speaker may be a subset of the identified speakers describedabove. For example, as shown in FIG. 11C, the user selection may includeselecting “John Rogers,” “Sarah Smith,” and “Clark Hamm.”

Returning to FIG. 11D, in response to receiving the user section at act1174, method 1170 may proceed to determine whether there is at least onetarget speaker selected, at act 1176. In response to determining that atleast one target speaker is selected, method 1160 may proceed totransmit the voice signature(s) associated with the selected targetspeaker(s) to the ear-worn device, at act 1178. For example, method 1160may enable the user to click “Send” button 1144 of FIG. 11C to transmitthe voice signature(s) to the ear-worn device. In another example, themethod may dynamically transmit the voice signature(s) to the ear-worndevice upon matching the extracted voice signature(s) to the registry ofknown speakers at act 1166, without waiting for any user selection. Inresponse to determining that no target speaker is selected, method 1160may stop. In some embodiments, in response to determining that no targetspeaker is selected, or no user selection is received, method 1160 maydisable the “Send” button 1144 of FIG. 11C (e.g., the “Send” button maybe grayed out), in which case, the electronic device will not transmitany voice signature to the ear-worn device.

As previously described, the ear-worn device may additionally storevoice signature(s) of one or more speakers. For example, see voicesignature storage 316 of FIG. 3 . In some embodiments, the voicesignature(s) stored in the ear-worn device may be voice signature(s) ofdefault target speaker(s). Alternatively, the voice signature(s) storedin the ear-worn device may be voice signature(s) of all known speakersto the wearer of the ear-worn device. Alternatively, the voicesignature(s) stored in the ear-worn device may be associated withspeakers with whom the wearer of the ear-worn device have frequentlyspoken, e.g., the voice signature(s) may be updated periodically ordynamically. It is appreciated that the voice signature(s) stored on theear-worn device may be uploaded/updated by a user interface (e.g., clickof buttons or a combination of buttons, or voice commands etc.) or viaan electronic device (e.g., a phone).

In case voice signatures of some speakers are stored on the ear-worndevice, acts 1128 of FIG. 11B and 1178 of FIG. 11D may instead transmitthe identifiers of selected target speaker(s) rather than the voicesignature(s) themselves. For example, the identifiers of selected targetspeaker(s) may include the speaker's names such as the names shown inlist 1102 of FIG. 11A and 1142 of FIG. 11C. In other examples, theidentifier(s) of selected speaker(s) may each include a fixed-lengthstring, a multiple-digit code, or any other suitable identifiers. Theear-worn device may use the identifier(s) to retrieve the associatedvoice signature(s) of the selected target speaker(s).

It is appreciated that the electronic device may be configured to enableto the user to edit the registry of known speakers at any time. In someembodiments, the registry may be edited to store only default speakers.In other embodiments, the registry may be edited to store known speakersto the wearer of the ear-worn device. In some embodiments, the registryon the electronic device may correspond to the voice signatures storedon the ear-worn device. In other embodiments, the registry on theelectronic device may be independent of the voice signatures stored onthe ear-worn device.

In FIG. 11E, updating the registry is further illustrated. In someembodiments, the user interface 1180 is triggered by a click of “Updateregistry” button 1150 of FIG. 11C. In the scenario previously describedin FIG. 11C, the voice signature of an un-identified speaker may beextracted from the input speech signal but not matched to any voicesignature of known speakers in the registry. In FIG. 11E, the userinterface 1180 prompts the user to enter the name of the new speaker viaa widget, such as an input box 1182. The user interface 1180 may receivea user entry of the name of the new identify. After entering the name,the user may click an “Update” button 1184 to update the registry withthe new entered name.

FIG. 11F illustrates an example process 1190 for implementing theexample graphical user interface of FIG. 11E, according to anon-limiting embodiment of the present application. In some embodiments,method 1190 may be implemented in an electronic device, e.g., 110 (inFIGS. 1 and 2 ), 304 (in FIG. 3 ). For example, method 1190 may beimplemented in the target speaker selector 1104 of FIG. 10 . In someembodiments, method 1190 may include receiving a user input for a newspeaker identity at act 1192. For example, the new speaker identity maybe entered by the user in an input box (e.g., 1182 of FIG. 11E) in auser interface. Method 1190 may further include adding the new speakeridentity to the registry at act 1194, and storing the voice signature ofthe new speaker associated with the registry at act 1196. Accordingly,with reference to FIG. 10 , the electronic device may store theunmatched voice signature in the voice signatures storage (e.g., 1010 ofFIG. 10 ), update the registry (e.g., 1008 of FIG. 10 ) with a new entryassociated with the new name, and associate the new entry with theunmatched voice signature.

FIG. 12 illustrates a block diagram of an example process 1200 forcollecting a voice signature of a speaker, according to a non-limitingembodiment of the present application. As described above, the voicesignatures of speakers may be pre-collected and stored on an electronicdevice for use with selecting the target speakers, as described in FIG.10 and various examples in FIGS. 11A-11F. In some embodiments, method1200 may be implemented in the voice signature collector 1012 of FIG. 10. Collecting the voice signatures of a plurality of speakers may includecollecting a respective audio segment for each of the plurality ofspeakers; generating a respective voice signature for each of theplurality of speakers using a neural network over the respective audiosegment; and registering in a registry the voice signatures of theplurality of speakers with the plurality of speakers in the contactlist. In some embodiments, in collecting voice signature of a speaker,method 1200 may include receiving an audio segment (e.g., a segment ofaudio signal) including the speech of the speaker at act 1204. The audiosegment may be recorded in a conversation including the speaker.Alternatively, and/or additionally, method 1200 may include outputting aprompt to the user at act 1202. For example, method 1200 may display aprompt (e.g., a script) and record the audio segment in respond to thespeaker reading the prompt.

Method 1200 may further process the audio segment with a voice signaturenetwork to extract a voice signature for the speaker at act 1206. Insome embodiments, the voice signature network may be implemented as 1002of FIG. 10 for execution on an electronic device. Method 1200 mayfurther store the extracted voice signature for the speaker and registerthe extracted voice signature in association with the speaker in aregistry.

Although method 1200 may be implemented in an external electronicdevice, such as a phone, in other embodiments, method 1200 may beperformed on a server (e.g., 306 of FIG. 3 ), or any other device. Insuch configuration, act 1204 may include receiving the audio segmentfrom an electronic device (e.g., a phone capable of recording), whichrecords the audio segment from the speaker. Act 1208 may includetransmitting the extracted voice signature to the electronic device forregistering with the registry.

FIG. 13 is a block diagram illustrating training and deploying 1300 of avoice signature machine learning model for extracting voice signaturefrom speech data, according to a non-limiting embodiment of the presentapplication. In some embodiments, a voice signature machine learningmodel training system 1302 may be configured to use training dataset1304 to generate a trained voice signature machine learning model 1306.The trained voice signature machine learning model 1306 may be deployedin voice signature network described above, such as 1002 of FIG. 10 .The trained voice signature machine learning model may also be used intraining the voice isolation network, as described in FIG. 8 (see 808 ofFIG. 8 ). In other embodiments, the trained voice signature machinelearning model may also be used to implement the voice signature networkfor collecting voice signatures, as described in FIG. 12 .

In some embodiments, the voice signature machine learning model 1306 mayinclude a neural network comprising a plurality of layers, each havingmultiple weights. Training system 1302 may use training dataset 1304 totrain the weights in the machine learning model 1306. In someembodiments, the training system 1302 may train the machine learningmodel 1306 using a contrastive learning method. The training dataset1304 may include pairs of audio clips including speech collected from aplurality of speakers. For example, multiple clips are collected fromvarious different speakers, with each clip being labelled with a uniquespeaker ID. The clips may be organized into positive 1310 and negativepairs 1312, where positive pairs denote pairs of clips belonging to thesame speaker while negative pairs denote clips belonging to differentspeakers. In some embodiments, the training dataset 1304 may containclean speech data (without noise). The training data may further beaugmented with added noise. For example, audio data in the trainingdataset may be augmented by mixing in background audio for a smallportion of the clips and applying room impulse responses to the speechto add reverberation.

In training the machine learning model 1306, a pre-trained voicesignature machine learning model is initialized and contains initialweights. The training system then provides the training datasetincluding the positive and negative clip pairs to the pre-trained voicesignature machine learning model to generate an output for each datapoint in each of a plurality of iterations in an optimization process.For example, each data point in an iteration may be a small segment ofthe clip pairs (e.g., in chunks of 1 second each). The training system1302 may provide the clip pairs through the voice signature machinelearning model 1306 and output pairs of embeddings. The optimizationprocess may be configured in such a way that embeddings corresponding toclips from the same speaker are made as similar as possible, whileembeddings corresponding to clips from different speakers are optimizedto be as different as possible.

In some embodiments, a contrastive loss function is applied directly tothe output (embeddings) corresponding to input audio clips. In anotherembodiment, a contrastive loss function is applied between eachembedding and the centroid of the corresponding cluster of embeddings inthe latent space. The similarity may be quantified using cosinesimilarity and the loss function may be configured such that theoptimization maximizes the cosine similarity for positive pairs andminimizes the cosine similarity for negative pairs. In some embodiments,the embedding (a 256-dimensional vector) may be obtained by averagingthe outputs corresponding to each chunk (e.g., 1 second).

The trained voice signature machine learning model 1306 may be deployed(executed) in the voice signature network described in variousembodiments above. The trained voice signature machine learning modelmay take any new audio speech signal 1314 and extract voice signature(s)of one or more speaker(s) 1316.

FIG. 14 illustrates an example of a computing system 1400 that may beimplemented in an electronic device to implement various embodimentsdescribed in the present application. In some embodiments, system 1400may implement operations described in various embodiments with referenceto FIGS. 1-3 and 10-13 on an electronic device, such as 110 (in FIGS. 1and 2 ) or 304 (in FIG. 3 ). In some embodiments, the system 1400includes one or more processors 1402 and one or more graphics processors1408, and may be a single processor desktop system, a multiprocessorworkstation system, or a server system having a large number ofprocessors 1402 or processor cores 1407. In on embodiment, the system1400 is a processing platform incorporated within a system-on-a-chip(SoC or SOC) integrated circuit for use in mobile, handheld, or embeddeddevices.

An embodiment of system 1400 can include or be incorporated within aserver-based smart-device platform or an online server with access tothe Internet. In some embodiments system 1400 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1400 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device (e.g., face-worn glasses), augmented reality device, orvirtual reality device. In some embodiments, data processing system 1400is a television or set top box device having one or more processors 1402and a graphical interface generated by one or more graphics processors1408.

In some embodiments, the one or more processors 1402 each include one ormore processor cores 1407 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1407 is configured to process aspecific instruction set 1409. In some embodiments, instruction set 1409may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1407 may each processa different instruction set 1409, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1407may also include other processing devices, such as a DSP.

In some embodiments, the processor 1402 includes cache memory 1404.Depending on the architecture, the processor 1402 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1402. In some embodiments, the processor 1402 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1407 using knowncache coherency techniques. A register file 1406 is additionallyincluded in processor 1402 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1402.

In some embodiments, processor 1402 is coupled to a processor bus 1410to transmit communication signals such as address, data, or controlsignals between processor 1402 and other components in system 1400. Inone embodiment the system 1400 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1416 and an Input Output(I/O) controller hub 1430. A memory controller hub 1416 facilitatescommunication between a memory device and other components of system1400, while an I/O Controller Hub (ICH) 1430 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1416 is integrated within the processor.

Memory device 1420 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 1420 can operate as system memory for the system 1400, to storedata 1422 and instructions 1421 for use when the one or more processors1402 executes an application or process. Memory controller hub 1416 alsocouples with an optional external graphics processor 1412, which maycommunicate with the one or more graphics processors 1408 in processors1402 to perform graphics and media operations.

In some embodiments, ICH 1430 enables peripherals to connect to memorydevice 1420 and processor 1402 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1446, afirmware interface 1428, a wireless transceiver 1426 (e.g., Wi-Fi,Bluetooth), a data storage device 1424 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1440 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1442 connect input devices, suchas keyboard and mouse 1444 combinations. A network controller 1434 mayalso couple to ICH 1430. In some embodiments, a high-performance networkcontroller (not shown) couples to processor bus 1410. It will beappreciated that the system 1400 shown is exemplary and not limiting, asother types of data processing systems that are differently configuredmay also be used. For example, the I/O controller hub 1430 may beintegrated within the one or more processor 1402, or the memorycontroller hub 1416 and I/O controller hub 1430 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1412.

Having described several embodiments of the techniques in detail,various modifications and improvements will readily occur to thoseskilled in the art. Such modifications and improvements are intended tobe within the spirit and scope of the invention. Accordingly, theforegoing description is by way of example only, and is not intended aslimiting. For example, any components described above (e.g., FIGS. 3-6C,8-10 and 13-14 ) may comprise hardware, software or a combination ofhardware and software. In a non-limiting example, voice isolationnetwork as described above, or a portion of the voice isolation network,may be implemented in software by a processor of the ear-worn device.Alternatively, the voice isolation network may be implemented inhardware, e.g., in an FPGA or an ASIC chip installed in the ear-worndevice.

FIG. 15 illustrate an example circuit including a voice isolationnetwork and a voice signature network coupled to the voice isolationwork, where the circuit (hardware-implemented or software-implemented)may be implemented in an ear-worn device, or in an external electronicdevice. For example, circuitry 1500 may be implemented in an ear-worndevice 108 (in FIGS. 1-2 ), 302 (in FIG. 3 ), or implemented as anexpanded voice isolation network 403 (in FIG. 4A), 450 (in FIG. 4B), 540(in FIG. 5A), 570 and 580 (in FIG. 5B), or 600 (in FIG. 6A). In someembodiments, circuitry 1500 may include a combination of configurationsspread between an ear-worn device and an electronic device, as describedabove in connection with other components of an audio system includingan ear-worn device and an electronic device. For example, circuitry 1500may include both a voice isolation network 1502 configured to isolatethe speech of speakers from an audio signal, and a voice signaturenetwork 1504 configured to provide the voice signature(s) of targetspeaker(s) to the voice isolation network 1502 from an audio signal.Voice isolation network 1502 may be configured in a similar manner asvoice isolation network 600 of FIG. 6A. Voice signature network 1504 maybe configured in a similar manner as voice signature network 1002 ofFIG. 10 , with the difference being that the circuitry 1000 (or aportion of circuitry 1000 including voice signature network 1002) may beimplemented in the ear-worn device. In such configuration, the inputaudio signal 1514 to the voice signature network 1504 may be provideddirectly by the ear-worn device and the output of the voice signaturenetwork may be provided directly to the voice isolation network 1502.The output audio signal from the voice isolation network 1502 may beprovided to other components in the ear-worn device for further processas previously described and output to the receiver(s) of the ear-worndevice.

In another variation, one or more components in an ear-worn devicedescribed above may be implemented in an electronic device. For example,circuit 1500 may be implemented in an external electronic device such as110 (in FIGS. 1-2 ), 304 (in FIG. 3 ). In such case, the input audiosignal 1512 to the voice isolation network 1502 may be transmitted fromthe ear-worn device or detected directly by the microphone of theelectronic device. The output audio signal 1518 of the voice isolationnetwork may transmitted to the ear-worn device for further processing asdescribe above, and output to the receiver(s) of the ear-worn device.

In other variations, the voice signatures may be extracted and/or storedon any suitable device. For example, the voice signatures of speakers ora subset of speakers (e.g., known speakers, or default target speakers)may be stored on the ear-worn device itself. Alternatively, and/oradditionally, voice signatures of some or all speakers may be extractedusing an on-board voice signature network on the ear-worn device,instead of on an external electronic device. In other variations, thevoice signature may be extracted without using a voice signaturenetwork. For example, a voice signature of a speaker may include afeature vector that contains the average speech power at differentfrequencies of an audio signal including the speech of the speaker. Thisvoice signature may be obtained using a traditional signal processingtechnique. Similarly, ear-worn device may have various user interfacesthat allow a wearer to make some user selections as described inconnection with FIGS. 11A-11F. For example, an ear-worn device may havea user interface including one or more buttons, one or more lights, anaudio interface, and/or a visual interface, which allows a user to makea variety of user selections in connection with one or more operationsof the ear-worn device, and/or one or more operations performed on theexternal electronic device described in various embodiments above.

In further variations, the voice isolation network may be adapted tohandle scenarios when multiple target speakers speak contemporaneouslyin a conversation, or when multiple target speakers and other non-targetspeaker(s) speak contemporaneously. In such cases, the voice isolationnetwork training system may re-arrange the training dataset or re-createthe training dataset to have clips that are mixed synthetically to havemultiple target speakers. The training system may train the machinelearning model with various permutations of multiple target speakers andmultiple non-target speakers in a given clip. Other methods may also bepossible.

As should be appreciated from the foregoing, according to someembodiments of the technology described herein, an ear-worn device isconfigured to use a voice isolation machine learning model that operateson a voice signature to isolate a component of a received speech signalrepresenting speech of a target speaker or non-target speaker associatedwith the voice signature. The voice signature may be obtained from aseparate machine learning model, such as a voice signature machinelearning model, configured to extract the voice signature from referenceaudio clips representing speech of the target or non-target speaker. Forexample, FIG. 15 shows that a voice signature network 1504 is configuredto process an audio signal (e.g., audio signal 2) to extract voicesignature(s) of target speaker(s) 1516. The voice isolation network 1502processes an audio signal (e.g., audio signal 1), in conjunction withthe voice signature(s) output by the voice signature network 1504, togenerate an output audio signal representing isolated speech of thetarget speaker(s). Such an embodiment may be advantageous for multiplereasons. As a non-limiting example, the voice signature network may beimplemented on an electronic device different from the ear-worn device,thereby reducing the computational complexity of implementing multiplemachine learning networks on the ear-worn device, while retaining voiceisolation capabilities. In addition, multiple voice signatures may bedetermined by the voice signature network and supplied to the voiceisolation network to allow separation of speech associated with multiplecontemporaneous speakers.

In further variations, a single network, such as a voice isolation andclassification network, may be configured to both (a) de-noise the inputaudio signal and (b) determine an embedding of the input audio signal.The embedding may be compared to a reference embedding representing avoice signature of a target or non-target speaker. The referenceembedding may be generated by passing a clip of speech from a targetspeaker or non-target speaker through the same network and averaging theresultant embeddings. The result of the comparison may be used toclassify the input audio signal as belonging to the target or non-targetspeaker, or as not belonging to any such target or non-target speaker.In some embodiments, the isolated component(s) of the audio signal areselectively processed based on the result of the classification. Forexample, an isolated component of the audio signal that is classified asoriginating from a target speaker may be amplified and/or enhanced,while an isolated component of the audio signal that is classified asoriginating from a non-target speaker may be suppressed.

In some embodiments, a voice isolation and classification network may beused instead of, or in addition to, a voice isolation network separatefrom a voice signature network. Such an embodiment may be advantageousfor multiple reasons. For example, instead of providing a voicesignature as input to a machine learning model and using the machinelearning model to identify and separate out speech associated with thevoice signature, the voice isolation and classification network may betrained to perform the two functions described above, namely (a)de-noising the input audio signal and (b) determining an embedding ofthe input audio signal. Determination of whether the input audio signalincludes speech of a target or non-target speaker may then beaccomplished using a comparator, such as a cosine similarity comparator.Such operation may be simpler than using a machine learning model toidentify speech associated with a target speaker by having the machinelearning model apply a voice signature as an input, and may lead tobetter overall performance of the hearing system. Also, using a voiceisolation and classification machine learning model that performs thefunctions (a) and (b) described above may allow for use of the samemodel on both the ear-worn device (e.g., hearing aid) and the separateelectronic device (e.g., mobile phone), which may simplify training anddeployment of the model compared to an embodiment in which separatemachine learning models are used for voice signature detection and voiceisolation. Moreover, an audio clip representing the voice of a speakermay be passed through the voice isolation and classification model once,and then used to predict whether subsequent input audio signalsrepresent speech from the same speaker. As another non-limiting example,layers of the voice isolation and classification machine learning modelcan take advantage of information already processed in previous frames.In other words, the layers that generate the discriminative embeddingcan also be recurrent, so while processing audio frame-by-frame inreal-time, the network can use recently received information to identifywho is speaking in a given frame (which is done by generating anembedding for a given frame).

FIG. 16 illustrates an example of a voice isolation network having avoice isolation model for de-noising an input audio signal anddetermining an embedding of the input audio signal. The illustratedvoice isolation network 1600 includes a voice isolation model 1602configured to receive an audio signal 1601. The voice isolation model1602 includes a voice isolation component 1604 and an embeddingcomponent 1606. Those two components may be considered separatecomponents of the same machine learning model, namely voice isolationmodel 1602. The audio signal 1601 is first processed by the voiceisolation component 1604 to produce a de-noised audio signal 1605. Thede-noised audio signal is provided to the embedding component 1604 whichdetermines an embedding 1610 of the audio signal. The voice isolationmodel 1602 therefore may provide two outputs, including the de-noisedaudio signal 1605 and the embedding 1610.

The illustrated voice isolation network may be used to generate a voicesignature of a target or non-target speaker, represented by theembedding 1610. The audio signal 1601 may be an audio clip of speech ofthe target or non-target speaker. Therefore, the embedding may be adiscriminative embedding representing a voice signature of the target ornon-target speaker. That discriminative embedding may then be used as areference embedding for later comparison to determine whether receivedaudio includes speech from the target or non-target speaker. In someembodiments, the audio clip of the speech will be processed through thenetwork frame by frame, and then the embedding averaged across all theframes will be the discriminative embedding that is stored for latercomparison. In some embodiments, the non-target speaker is the wearer ofthe ear-worn device, and the embedding 1610 may represent a voicesignature of the wearer of the ear-worn device.

FIG. 17 illustrates a voice isolation and classification networkaccording to some embodiments of the present technology. The illustratedvoice isolation and classification network 1700 includes a voiceisolation model 1702 having a voice isolation component 1704 and anembedding component 1706. In some embodiments, the voice isolation model1702 is the same model as voice isolation model 1602, but the two may bedifferent instances of the same machine learning model in differentlocations. For instance, the voice isolation model 1602 may be used onan electronic device (e.g., a mobile phone) and the voice isolationmodel 1702 may be used on an ear-worn device (e.g., a hearing aid).

The voice isolation model 1702 receives an audio signal 1701 as inputand generates a de-noised audio signal 1705 using the voice isolationcomponent 1704. The voice isolation model 1702 further determines anembedding 1710 using the embedding component 1706. The voice isolationmodel 1702 therefore may output the de-noised audio signal 1705 and theembedding 1710.

The voice isolation and classification network 1700 further comprises acomparator 1712. The comparator 1712 is configured to compare theembedding 1710 with a reference embedding 1714. The reference embeddingmay be provided by a separate instance of the voice isolation model, forexample by voice isolation model 1602. In some embodiments, then, thevoice isolation model 1602 may be used to generate an embedding 1610which may be stored and used as a reference embedding for use by adifferent instance of the voice isolation model. In one embodiment, forexample, the voice isolation model 1602 is used on a mobile phone todetermine a voice signature of a target or non-target speakerrepresented by embedding 1610. The embedding 1610 is then provided to anear-worn device having the voice isolation and classification network1700, to be used as the reference embedding 1714. The embedding 1610 insome embodiments represents a voice signature of the wearer of theear-worn device.

The comparator 1712 compares the embedding 1710 with the referenceembedding 1714 (e.g., embedding 1610 from FIG. 16 ) and determineswhether they match. If the embedding 1710 matches the referenceembedding 1714, the comparator 1712 outputs a classification value 1716(e.g., a 1) indicating the match and thus that the audio signal 1701includes speech from a speaker associated with reference embedding 1714.In some embodiments, the match may be determined by the cosinesimilarity between the embedding vectors, which may involve calculatingthe cosine similarity between the embedding vectors. In otherembodiments, this may be another small recurrent network that predictsthe degree of match. If the embedding 1710 does not match the referenceembedding 1714, the comparator 1712 outputs a classification value 1716(e.g., a 0) indicating no match and thus that the audio signal 1701 doesnot include speech from the speaker associated with the referenceembedding 1714.

The classification value 1716 is provided to a relative gain filter 1718which processes the de-noised audio signal 1705. For example, if theclassification value 1716 indicates a match between the embedding 1710and the reference embedding 1714, output audio signal 1720 may beenhanced or attenuated according to user preferences for the speakerassociated with the reference embedding 1714. For example, if thereference embedding 1714 represents the wearer's own voice signature,indication of a match between the embedding 1710 and the referenceembedding 1714 may result in the relative gain filter 1718 outputting anattenuated output audio signal 1720. In this manner, the wearer's ownvoice may be suppressed and the wearer may have a more positiveexperience with the hearing aid.

The output audio signal 1720 may be provided to a DSP (e.g., see FIGS.4A and 4B) for outputting to the receiver(s) of the ear-worn device.

The comparator 1712 may be considered part of or separate from the voiceisolation model. In the representation of FIG. 17 , the comparator 1712is shown as separate from the voice isolation model 1702. However, inalternative embodiments, the comparator 1712 may be considered to formpart of the voice isolation model.

According to an embodiment of the present application, the voiceisolation model 1602 and the voice isolation and classification network1700 are used together. The voice isolation model 1602 is used on anelectronic device (e.g., electronic device 110) to generate an embedding1610 representing a target or non-target speaker. The de-noised audiosignal 1605 may not be used. The voice isolation and classificationnetwork 1700 is used on an ear-worn device (e.g., ear-worn device 108,which may be a hearing aid) and receives the embedding 1610 from theelectronic device for use as reference embedding 1714. Thus, it shouldbe appreciated that in some embodiments the same voice isolation modelmay be used on both the electronic device and the ear-worn device. Thissystem architecture simplifies development and training of the machinelearning model compared to a hearing system that uses different machinelearning models on the electronic device and ear-worn device.

Training of the voice isolation model 1602 and voice isolation model1702—which, again, may be different instances of the same machinelearning model—may be done in stages. First, the model may be trained todo the task of voice isolation. After voice isolation layers aretrained, then additional layers are trained to check whether theisolated voice is a match for a given snippet of voice audio. Thetrained model would therefore output both clean speech and the result ofa classifier that matched the speech to a voice signature. The trainedmodel may then be used to amplify or suppress the voice. For example,when the target voice is present, the voice stream can be played out atfull volume. When the classifier determines that a different speaker isspeaking, the voice stream can be suppressed.

As described above, such an approach may provide various benefits. Forexample, the voice signature clip the represents the target ornon-target voice can be passed through the voice isolation network justonce, and then stored as a single vector representing the averageembedding for the entire voice signature clip. The subsequently receivedinput audio signal may be processed frame by frame with low latency(e.g., in real time), and the voice isolation and classification networkcan predict the discriminative embedding. Also, since the machinelearning model may be a recurrent network, the predicted embedding for agiven frame of the input audio signal can utilize information fromprevious frames.

The voice isolation networks of FIGS. 16 and 17 may also be used withmultiple target or non-target speakers. To do so, just the classifierlayers of the voice isolation and classification model are rerun foreach target or non-target speaker. The layers of the machine learningmodel that perform de-noising may be run once for each audio frame.Since the layers that perform de-noising may be computationallyexpensive, such operation may allow for relatively computationallylittle effort to process input audio signals to identify multiple targetor non-target speakers.

The various embodiments described in FIGS. 1-17 provide advantages overconventional hearing aids. For example, rather than separating outspeech from noise as done in conventional hearing aids (in which allvoices may be presented equally to the wearer), techniques describedherein allow a system to selectively isolate one or more target speakersfrom other non-target speakers or noise, thus provide a positiveexperience for the wearer of the ear-worn device. Such positiveexperience allows the wearer to focus on the speech of a subset of thevoices in the environment, which is a function important to naturalhearing.

Further, the use of voice signatures in a voice isolation network allowsthe system to selectively isolate the target speakers. Other techniques,such as the use of triplets of clips combined with the use of a trainedvoice signature machine learning model in a training system yields atrained voice isolation machine learning model with improved performanceon isolating speech(es) of target speaker(s).

Various techniques are provided to further process the isolated speechby preferentially treating the isolated speech, to generate enhancedspeech for target speakers with increased SNR. Other techniques includedynamically controlling the voice isolation network to activate ordeactivate during a conversation. Such controlling results in a savingof computation, which makes it possible to execute a machine learningmodel in real-time and on a power-restraint ear-worn device.

Even further, the system uses a phone associated with the wearer of theear-worn device, which allows the wearer to effectively select targetspeakers to whom the wearer of the ear-worn device prefers to listen.For example, the phone may store a registry of known speakers, which maybe updated by the user. The registry of known speakers thus assists theuser to quickly select target speakers in a multi-speaker conversation.

According to some embodiments, a method of operating a mobile processingdevice operatively couplable to an ear-worn device is provided. Themethod comprises: wirelessly transmitting, from the mobile processingdevice to the ear-worn device, a voice signature of at least one targetspeaker.

According to some embodiments, an apparatus is provided. The apparatuscomprises at least one processor; and at least one non-transitorycomputer-readable medium storing instructions that, when executed, causethe at least one processor to perform a method of operating a mobileprocessing device operatively couplable to an ear-worn device. Themethod comprises: wirelessly transmitting, from the mobile processingdevice to the ear-worn device, a voice signature of at least one targetspeaker.

According to some embodiments, a non-transitory computer-readable mediumcomprising instructions that, when executed, cause at least oneprocessor to perform a method of operating a mobile processing deviceoperatively couplable to an ear-worn device. The method comprises:wirelessly transmitting, from the mobile processing device to theear-worn device, a voice signature of at least one target speaker.

In some embodiments, the voice signature comprises a feature vector, andwirelessly transmitting the voice signature of the target speakercomprises wirelessly transmitting the feature vector.

Some embodiments further comprise storing a registry of plurality ofspeakers including the target speaker on the mobile processing device,wherein the registry comprises a plurality of entries respectively eachassociated with a voice signature of a respective speaker of theplurality of speakers; wherein transmitting the voice signature of thetarget speaker comprises transmitting an identifier identifying thevoice signature of the target speaker in the registry.

Some embodiments further comprise receiving an input speech signalincluding speech from the target speaker; and determining that the inputspeech signal includes the speech from the target speaker by processingthe input speech signal with a machine learning model that isolatesspeech associated with the voice signature of the target speaker.

In some embodiments, receiving the input speech signal comprisesreceiving the input speech signal from a microphone coupled to themobile processing device or from the ear-worn device.

Some embodiments further comprise determining that the input speechsignal includes speech from an additional speaker besides the targetspeaker; and wirelessly transmitting from the mobile processing deviceto the ear-worn device, a voice signature of the additional speaker.

Some embodiments further comprise receiving a user selection identifyingthe target speaker and/or the additional speaker, and wherein wirelesslytransmitting the voice signature of the target speaker and/or wirelesslytransmitting the voice signature of the additional speaker is performedin response to receiving the user selection.

Some embodiments further comprise, before receiving the user selectionidentifying the target speaker and/or the additional speaker, displayingrespective entries of the target speaker and/or the additional speakerin the registry.

Some embodiments further comprise receiving an input speech signalincluding speech from the target speaker; and obtaining the voicesignature of the target speaker based on the input speech signal.

In some embodiments, obtaining the voice signature of the target speakercomprises developing the voice signature of the target speaker byprocessing the input speech signal with a machine learning model.

In some embodiments, the mobile processing device comprises amicrophone, and wherein receiving the input speech signal comprisesreceiving the input speech signal from the microphone.

In some embodiments, receiving the input speech signal compriseswirelessly receiving the input speech signal from the hearing aid.

Some embodiments further comprise receiving a user selection identifyingthe target speaker, and wherein wirelessly transmitting the voicesignature of the target speaker is performed in response to receivingthe user selection.

In some embodiments, the mobile processing device stores a plurality ofvoice signatures including the voice signature of the target speaker.Some embodiments further comprise, in advance of receiving the userselection identifying the target speaker, presenting the user with anoption to select the target speaker.

In some embodiments, each voice signature in the plurality of voicesignatures is associated with a respective speaker among a plurality ofspeakers in a contact list including the target speaker, and storing theplurality of voice signatures comprises: collecting a respective audiosegment for each of the plurality of speakers; generating a respectivevoice signature for each of the plurality of speakers using a machinelearning model over the respective audio segment; and registering in aregistry the voice signatures of the plurality of speakers with theplurality of speakers in the contact list.

In some embodiments, collecting the respective audio segment for aspeaker of the plurality of speakers comprises: displaying a prompt;recording the respective audio segment in response to the speakerreading the prompt.

In some embodiments, collecting the respective audio segment for aspeaker of the plurality of speakers comprises processing an audiosignal recorded in a conversation including the speaker.

According to some embodiments, a method of selectively processing, withan ear-worn device including a processor and a microphone coupled to theprocessor, a target speaker's speech from an audio signal comprisingtemporally overlapping speech components from multiple speakers isprovided. In some embodiments, the target speaker comprises a wearer ofthe ear-worn device. The method comprises: detecting the audio signalwith the microphone of the ear-worn device; providing the audio signaldetected by the microphone of the ear-worn device to the processor ofthe ear-worn device; isolating, with the processor of the ear-worndevice, a component of the audio signal representing the targetspeaker's speech from among the temporally overlapping speech componentsfrom multiple speakers by processing the audio signal with a machinelearning model; and suppressing the isolated component of the audiosignal representing the target speaker's speech.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified.

The terms “approximately” and “about” may be used to mean within ±20% ofa target value in some embodiments, within ±10% of a target value insome embodiments, within ±5% of a target value in some embodiments, andyet within ±2% of a target value in some embodiments. The terms“approximately” and “about” may include the target value.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

Having described above several aspects of at least one embodiment, it isto be appreciated various alterations, modifications, and improvementswill readily occur to those skilled in the art. Such alterations,modifications, and improvements are intended to be object of thisdisclosure. Accordingly, the foregoing description and drawings are byway of example only.

What is claimed is:
 1. A hearing aid system, comprising: an ear-worndevice including: a microphone configured to receive an audible signaland output an electrical signal representing the audible signal;front-end circuitry coupled to the microphone and configured to receivethe electrical signal representing the audible signal, digitize theelectrical signal, and output a digitized version of the audible signal;a controller configured to: receive the digitized version of the audiblesignal; and selectively output the digitized version of the audiblesignal to either a digital signal processor (DSP) or a neural networkengine comprising a voice isolation and classification neural network;the neural network engine, wherein the neural network engine is coupledto an output of the controller and configured to: output from the voiceisolation and classification neural network a de-noised version of thedigitized version of the audible signal; output from the voice isolationand classification neural network an embedding of the digitized versionof the audible signal; compare the embedding of the digitized version ofthe audible signal to a reference embedding; separate the digitizedversion of the audible signal into multiple source signals using thevoice isolation and classification neural network; apply gains tomultiple source signals of the de-noised version of the digitizedversion of the audible signal based at least in part on a result of thecomparison of the embedding of the digitized version of the audiblesignal to the reference embedding; create a combined signal byrecombining the multiple source signals after application of the gains;and provide the combined signal to the DSP; the DSP, wherein the DSP iscoupled to the output of the controller and an output of the neuralnetwork engine, and wherein the DSP is configured to, upon receiving thecombined signal from the neural network engine, filter the combinedsignal to generate an output signal; and a speaker, coupled to an outputof the DSP and configured to playback the output signal in audible form;and an electronic device, comprising a voice isolation andclassification neural network, wherein the electronic device isconfigured to: receive a speech sample from a target speaker andgenerate the reference embedding; and provide the reference embedding tothe ear-worn device.
 2. The hearing aid system of claim 1, wherein thevoice isolation and classification neural network of the ear-worn deviceand the voice isolation and classification neural network of theelectronic device are of a same type.
 3. The hearing aid system of claim2, wherein the voice isolation and classification neural network of theear-worn device is a recurrent neural network.
 4. The hearing aid systemof claim 2, wherein the electronic device is configured to provide agraphical user interface (GUI) to a user and to provide the referenceembedding to the ear-worn device upon selection by the user of thetarget speaker on the GUI.
 5. The hearing aid system of claim 1, whereinthe electronic device is configured to provide a graphical userinterface (GUI) to a user and to provide the reference embedding to theear-worn device upon selection by the user of the target speaker on theGUI.
 6. The hearing aid system of claim 1, wherein the referenceembedding is a first reference embedding, and wherein the electronicdevice is configured to received multiple speech samples from respectivetarget speakers and to generate respective reference embeddingsincluding the first reference embedding using the voice isolation andclassification neural network of the electronic device, wherein theelectronic device is further configured to provide the respectivereference embeddings to the ear-worn device, and wherein the neuralnetwork engine of the ear-worn device is further configured to comparethe embedding of the digitized version of the audible signal to therespective reference embeddings.
 7. The hearing aid system of claim 6,wherein the neural network engine of the ear-worn device is configuredto compare the embedding of the digitized version of the audible signalto the respective reference embeddings at least in part by calculating arespective cosine similarity between the embedding of the digitizedversion of the audible signal and a respective reference embedding. 8.The hearing aid system of claim 7, wherein the neural network engine isconfigured to apply the gains to multiple source signals of thede-noised version of the digitized version of the audible signal basedat least in part on results of the comparisons of the embedding of thedigitized version of the audible signal to the respective referenceembeddings.
 9. The hearing aid system of claim 1, wherein the referenceembedding represents a voice of a wearer of the ear-worn device, andwherein, when the comparison of the embedding of the digitized versionof the audible signal to the reference embedding indicates that thewearer's voice is present in the digitized version of the audiblesignal, the neural network engine is configured to reduce a component ofthe digitized version of the audible signal representing the wearer'svoice.
 10. The hearing aid system of claim 1, wherein the electronicdevice is a smartphone.
 11. The hearing aid system of claim 1, whereinthe neural network engine of the ear-worn device is configured tocompare the embedding of the digitized version of the audible signal tothe reference embedding at least in part by calculating a cosinesimilarity between the embedding of the digitized version of the audiblesignal and the reference embedding.
 12. The hearing aid system of claim1, wherein the front-end circuitry, controller, neural network engine,and DSP are implemented on a system-on-chip.
 13. The hearing aid systemof claim 1, wherein the controller is further configured to determine aheuristic of the digitized version of the audible signal, and whereinthe controller is further configured to selectively output the digitizedversion of the audible signal to either the DSP or the neural networkengine depending at least in part on the heuristic of the digitizedversion of the audible signal.
 14. The hearing aid system of claim 1,wherein the controller is further configured to receive a user-selectedmode and to selectively output the digitized version of the audiblesignal to either the DSP or the neural network engine depending at leastin part on the user-selected mode.
 15. The hearing aid system of claim14, wherein the electronic device is further configured to provide tothe ear-worn device the user-selected mode.
 16. The hearing aid systemof claim 1, wherein the neural network engine is further configured toreceive an indication of a user-selected directionality and to selectthe gains based at least in part on the user-selected directionality.17. The hearing aid system of claim 1, wherein the DSP is configured toapply frequency-dependent filtering including the application ofnon-linear gains to different frequency bands of the combined signal.18. The hearing aid system of claim 1, wherein the controller isconfigured to provide the digitized version of the audible signal to theneural network engine in segments, and wherein the neural network engineis configured to process a segment of the digitized version of theaudible signal in a time less than or equal to a duration of thesegment.
 19. The hearing aid system of claim 1, wherein the voiceisolation and classification neural network of the ear-worn device is arecurrent neural network.
 20. The hearing aid system of claim 1, whereinthe voice isolation and classification neural network of the ear-worndevice is a convolutional neural network.